NMTC and LIHTC Evaluation

Introduction

The differences-in-differences model (sometimes called diff-in-diff or DiD) is a regression model that studies the difference in trends over time between a treatment group and a control group. The DiD model’s most important assumption is that, without treatment, trends in the treatment group would be similar to trends in the control group. This is how the counterfactual and program effect are established and calculated. Figarri Kiesha’s DiD diagram (2022) visualizes this.

So why use the DiD model? Studying how two tax programs affect social vulnerability is complicated by many factors. The DiD model controls for observed and unobserved time-invariant characteristics, or factors that consistently influence the data over time (Gertler et. al, 2016, p. 134). Additionally, we have data that we can study before and after the inventions of the NMTC and LIHTC programs. The data also contains information for counties that were eligible for the tax credit programs but received no project funding. These counties serve as a similar control group and will help to create the counterfactual.

Dependent Variables

This report uses several DiD models to understand the impact of the tax credit programs on social vulnerability, median income, median home value, and the house price index. These four variables will serve as the dependent (or outcome) variables. We use these variables because they overlap with the tax credit programs’ purpose to invest in affordable housing to benefit low-income households and community development to benefit disadvantaged neighborhoods. It’s reasonable to expect that these investments could improve SVI and median income. Because the NMTC targets neighborhoods, we expect to see home value increase.

dep_vars %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Variable	Description
Social Vulnerability Index (SVI)	An index that measures 16 demographic and socioeconomic variables to understand a community’s risk to hazards
Median income	The average income at which half of residents earn below and half of residents earn above
Median home value	The average home price at which half of listings are listed below and half of listings are listed above
House Price Index (HPI)	An index that measures "average price changes in repeat sales or refinancings on the same properties" (FHFA, 2025)

Independent Variables

The New Markets Tax Credit (NMTC) Program “incentivizes community development and economic growth through the use of tax credits that attract private investment to distressed communities” (U.S. Department of the Treasury, 2025). The data includes variables about project awards (in dollars), the number of projects, geography, and more. Because it includes geographic data, we can pair the NMTC data with other data, specifically from the American Community Survey.

The Low-Income Housing Tax Credit (LIHTC) Program allows authorized agencies to “issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households” (U.S. Department of Housing and Urban Development, 2025). Like the NMTC, the LIHTC data includes variables about project awards (in dollars), the number of projects, and geography, which makes it useful for pairing with ACS data.

Library

# Load packages
library(here)         # relative filepaths for reproducibility
library(rio)          # read excel file from URL
library(tidyverse)    # data wrangling
library(stringi)      # string data wrangling
library(tidycensus)   # US census data
library(ggplot2)      # data visualization
library(scales)       # palette and number formatting
library(unhcrthemes)  # data visualization themes
library(ggrepel)      # data visualization formatting to avoid overlapping
library(rcompanion)   # data visualization of variable distribution
library(ggpubr)       # data visualization of variable distribution
library(moments)      # measures of skewness and kurtosis
library(tinytable)    # format regression tables
library(modelsummary) # format regression tables

Load Functions

import::here( "fips_census_regions",
              "load_svi_data",
              "merge_svi_data",
              "census_division",
              "slopegraph_plot",
              "census_pull",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps_Jazzy.R"),
             .character_only = TRUE)

# Load API key, assign to TidyCensus Package
source(here::here("analysis/password.R"))
census_api_key(.census_api_key)

## To install your API key for use in future sessions, run this function with `install = TRUE`.

Data

# Load NMTC AND LIHTC data sets
svi_divisional_nmtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_nmtc.rds")))

svi_national_nmtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_nmtc.rds")))

svi_divisional_lihtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_lihtc.rds")))

svi_national_lihtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_lihtc.rds")))

House Price Index Data

hpi_df <- read.csv("https://r-class.github.io/paf-515-course-materials/data/raw/HPI/HPI_AT_BDL_tract.csv")

hpi_df_10_20 <- hpi_df %>% 
  mutate(GEOID10 = str_pad(tract, 11, "left", pad=0)) %>% 
  filter(year %in% c(2010, 2020))  %>%
 select(GEOID10, state_abbr, year, hpi) %>%
  pivot_wider(names_from = year, values_from = hpi) %>%
  mutate(housing_price_index10 = `2010`,
         housing_price_index20 = `2020`) %>%
  select(GEOID10, state_abbr, housing_price_index10, housing_price_index20)

# View data
hpi_df_10_20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID10	state_abbr	housing_price_index10	housing_price_index20
01001020100	AL	132.35	152.78
01001020200	AL	123.78	123.37
01001020300	AL	158.57	167.01
01001020400	AL	165.11	179.60
01001020501	AL	172.55	180.96
01001020502	AL	158.75	164.25

# Drop state_abbr column for joining
hpi_df_10_20 <- hpi_df_10_20 %>% select(-state_abbr)

CBSA Crosswalk Data

msa_csa_crosswalk <- rio::import("https://r-class.github.io/paf-515-course-materials/data/raw/CSA_MSA_Crosswalk/qcew-county-msa-csa-crosswalk.xlsx", which=4)

msa_csa_crosswalk <- msa_csa_crosswalk %>% 
  mutate(county_fips = str_pad(`County Code`, 5, "left", pad=0),
         cbsa = coalesce(`CSA Title`, `MSA Title`),
         cbsa_code = coalesce(`CSA Code`, `MSA Code`),
         county_title = `County Title`)  %>% 
  select(county_fips, county_title, cbsa, cbsa_code)

msa_csa_crosswalk %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

county_fips	county_title	cbsa	cbsa_code
01001	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01003	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01005	Barbour County, Alabama	Eufaula, AL-GA MicroSA	C2164
01007	Bibb County, Alabama	Birmingham-Hoover-Cullman, AL CSA	CS142
01009	Blount County, Alabama	Birmingham-Hoover-Cullman, AL CSA	CS142
01015	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150

Census Data

states <- list(svi_national_nmtc$state %>% unique())
states 

## [[1]]
##  [1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "DC" "FL" "GA" "HI" "ID" "IL" "IN"
## [16] "IA" "KS" "KY" "LA" "ME" "MD" "MA" "MI" "MN" "MS" "MO" "MT" "NE" "NV" "NH"
## [31] "NJ" "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN" "TX" "UT"
## [46] "VT" "VA" "WA" "WV" "WI" "WY"

census_pull10 <- lapply(states, census_pull, yr = 2010)

census_pull10_df <- census_pull10[[1]] %>%  
  # Drop margin of error column
  select(-moe) %>%
  # Add suffix to variable names
  mutate(variable = paste0(variable, "_10")) %>%
  # Pivot data frame
  pivot_wider(
    names_from = variable,
    values_from = c(estimate)
  )

census_pull10_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID	NAME	Median_Income_10	Median_Home_Value_10
01001020100	Census Tract 201, Autauga County, Alabama	31769	120700
01001020200	Census Tract 202, Autauga County, Alabama	19437	138500
01001020300	Census Tract 203, Autauga County, Alabama	24146	111300
01001020400	Census Tract 204, Autauga County, Alabama	27735	126300
01001020500	Census Tract 205, Autauga County, Alabama	35517	173000
01001020600	Census Tract 206, Autauga County, Alabama	24597	110700
01001020700	Census Tract 207, Autauga County, Alabama	22114	93800
01001020801	Census Tract 208.01, Autauga County, Alabama	30841	258000
01001020802	Census Tract 208.02, Autauga County, Alabama	29006	145100
01001020900	Census Tract 209, Autauga County, Alabama	24841	108000

census_pull19 <- lapply(states, census_pull, yr = 2019)

census_pull19_df <- census_pull19[[1]] %>% 
  # Select columns
  select(GEOID, NAME, variable, estimate, moe) %>% 
  # Create individual FIPS columns for state, county, and tract
  mutate(FIPS_st = substr(GEOID, 1, 2),
         FIPS_county = substr(GEOID, 3, 5),
         FIPS_tract = substr(GEOID, 6, 11)) %>%
# Los Angeles, CA Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "037" & FIPS_st == "06" & FIPS_tract == "137000"), "930401", FIPS_tract )) %>%
# Pima County, AZ Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "002704"), "002701", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "002906"), "002903", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004118"), "410501", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004121"), "410502", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004125"), "410503", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "005200"), "470400", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "005300"), "470500", FIPS_tract2 )) %>%
# Madison County, NY Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030101"), "940101", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030102"), "940102", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030103"), "940103", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030200"), "940200", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030300"), "940300", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030401"), "940401", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030403"), "940403", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030600"), "940600", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030402"), "940700", FIPS_tract2 )) %>%
# Oneida County, NY Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024800"), "940000", FIPS_tract2 )) %>% 
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024700"), "940100", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024900"), "940200", FIPS_tract2 )) %>%  
                      # Move columns in data set
                      relocate(c(FIPS_st, FIPS_county, FIPS_tract, FIPS_tract2),.after = GEOID) %>%
                      # Create new GEOID column
                      mutate(GEOID = paste0(FIPS_st, FIPS_county, FIPS_tract2)) %>% 
                      # Drop newly created FIPS columns and margin of error
                      select(-FIPS_st, -FIPS_county, -FIPS_tract, -FIPS_tract2, -moe) %>% 
                      # Add suffix
                      mutate(variable = paste0(variable, "_19")) %>%
                      # Pivot data set
                      pivot_wider(
                        names_from = variable,
                        values_from = c(estimate)
                      ) 

census_pull19_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID	NAME	Median_Income_19	Median_Home_Value_19
01001020100	Census Tract 201, Autauga County, Alabama	25970	136100
01001020200	Census Tract 202, Autauga County, Alabama	20154	90500
01001020300	Census Tract 203, Autauga County, Alabama	27383	122600
01001020400	Census Tract 204, Autauga County, Alabama	34620	152700
01001020500	Census Tract 205, Autauga County, Alabama	41178	186900
01001020600	Census Tract 206, Autauga County, Alabama	21146	103600
01001020700	Census Tract 207, Autauga County, Alabama	20934	82400
01001020801	Census Tract 208.01, Autauga County, Alabama	31667	322900
01001020802	Census Tract 208.02, Autauga County, Alabama	33086	171500
01001020900	Census Tract 209, Autauga County, Alabama	32677	156900

inflation_adj = 1.16

# Join 2010 and 2019 Median Income and Home Value Data
census_pull_df <- left_join(census_pull10_df, census_pull19_df[c("GEOID", "Median_Income_19", "Median_Home_Value_19")], join_by("GEOID" == "GEOID"))

# Create new inflation adjusted columns for 2010 median income and median home value, find changes over time
census_pull_df <- census_pull_df %>% 
                   mutate(Median_Income_10adj = Median_Income_10*inflation_adj,
                          Median_Home_Value_10adj = Median_Home_Value_10*inflation_adj,
                          Median_Income_Change = Median_Income_19 - Median_Income_10adj,
                          Median_Income_Change_pct = (Median_Income_19 - Median_Income_10adj)/Median_Income_10adj,
                          Median_Home_Value_Change = Median_Home_Value_19 - Median_Home_Value_10adj,
                          Median_Home_Value_Change_pct = (Median_Home_Value_19 - Median_Home_Value_10adj)/Median_Home_Value_10adj)

# View data
census_pull_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct
01001020100	Census Tract 201, Autauga County, Alabama	31769	120700	25970	136100	36852.04	140012	-10882.04	-0.2952900	-3912	-0.0279405
01001020200	Census Tract 202, Autauga County, Alabama	19437	138500	20154	90500	22546.92	160660	-2392.92	-0.1061307	-70160	-0.4366986
01001020300	Census Tract 203, Autauga County, Alabama	24146	111300	27383	122600	28009.36	129108	-626.36	-0.0223625	-6508	-0.0504074
01001020400	Census Tract 204, Autauga County, Alabama	27735	126300	34620	152700	32172.60	146508	2447.40	0.0760709	6192	0.0422639
01001020500	Census Tract 205, Autauga County, Alabama	35517	173000	41178	186900	41199.72	200680	-21.72	-0.0005272	-13780	-0.0686665
01001020600	Census Tract 206, Autauga County, Alabama	24597	110700	21146	103600	28532.52	128412	-7386.52	-0.2588807	-24812	-0.1932218
01001020700	Census Tract 207, Autauga County, Alabama	22114	93800	20934	82400	25652.24	108808	-4718.24	-0.1839309	-26408	-0.2427027
01001020801	Census Tract 208.01, Autauga County, Alabama	30841	258000	31667	322900	35775.56	299280	-4108.56	-0.1148426	23620	0.0789227
01001020802	Census Tract 208.02, Autauga County, Alabama	29006	145100	33086	171500	33646.96	168316	-560.96	-0.0166719	3184	0.0189168
01001020900	Census Tract 209, Autauga County, Alabama	24841	108000	32677	156900	28815.56	125280	3861.44	0.1340054	31620	0.2523946

NMTC Data

svi_divisional_nmtc_df0 <- left_join(svi_divisional_nmtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_divisional_nmtc_df1 <- left_join(svi_divisional_nmtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_divisional_nmtc_df <- left_join(svi_divisional_nmtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_divisional_nmtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_dollars_formatted	post10_nmtc_project_cnt	post10_nmtc_dollars	post10_nmtc_dollars_formatted	nmtc_flag	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
02013000100	02013	000100	AK	Alaska	Aleutians East Borough	4	West Region	9	Pacific Division	3703	474	267	1212	3695	32.80108	0.7570	1	111	3163	3.509327	0.08691	0	25	158	15.82278	0.01337	0	17	109	15.59633	0.02605	0	42	267	15.73034	0.004754	0	1082	3017	35.863441	0.85420	1	2060	3112	66.195373	0.99990	1	127	3.429652	0.042400	315	8.506616	0.03961	0	182	2849	6.388206	0.077750	0	50	165	30.30303	0.8835	1	1070	3617	29.5825270	0.93700	1	3492	3703	94.30192	0.9141	1	474	8	1.687764	0.29250	0	42	8.8607595	0.8128	1	7	267	2.6217228	0.4003	0	77	267	28.8389513	0.96850	1	2969	3703	80.1782339	0.9940	1	2.702764	0.5611	3	1.980260	0.23800	2	0.9141	0.9047	1	3.46810	0.8902	3	9.065224	0.6397	9	3389	1199	988	698	3379	20.65700	0.5925	0	86	2414	3.562552	0.2665	0	67	607	11.037891	0.01803	0	74	381	19.42257	0.04067	0	141	988	14.27126	0.006988	0	354	2646	13.378685	0.61070	1345	3384	39.745863	0.99970	1	381	11.2422544	0.31390	443	13.07170	0.0988	0	339	2941.000	11.526692	0.386000	0	135	593.000	22.765599	0.7920	1	334	3276	10.1953602	0.72620	2939	3389.000	86.72175	0.8110	1	1199	38	3.169308	0.3474	0	69	5.754796	0.7806	1	30	988	3.0364372	0.36010	0	220	988.000	22.267207	0.9527	1	1035	3389	30.5399823	0.9843	1	2.476388	0.4947	1	2.316900	0.37850	1	0.8110	0.8038	1	3.42510	0.8683	3	9.029388	0.6419	6	Yes	\$0	1	15762500	\$15,762,500	1	Census Tract 1, Aleutians East Borough, Alaska	21138	121600	29177	119900	24520.08	141056	4656.92	0.1899227	-21156	-0.1499830	NA	NA	NA	NA	NA
02016000100	02016	000100	AK	Alaska	Aleutians West Census Area	4	West Region	9	Pacific Division	1774	1056	166	328	1231	26.64500	0.6553	0	15	1370	1.094890	0.01369	0	25	95	26.31579	0.09653	0	16	71	22.53521	0.05099	0	41	166	24.69880	0.029080	0	207	1330	15.563910	0.58390	0	484	973	49.743063	0.99520	1	53	2.987599	0.031800	182	10.259301	0.05188	0	147	747	19.678715	0.864200	1	19	96	19.79167	0.6606	0	79	1718	4.5983702	0.46890	0	1154	1774	65.05073	0.6522	0	1056	22	2.083333	0.31610	0	0	0.0000000	0.2497	0	10	166	6.0240964	0.6154	0	84	166	50.6024096	0.99320	1	1324	1774	74.6335964	0.9935	1	2.277170	0.4443	1	2.077380	0.27780	1	0.6522	0.6454	0	3.16790	0.7874	2	8.174650	0.5311	4	950	694	199	218	719	30.31989	0.7848	1	15	560	2.678571	0.1560	0	11	117	9.401709	0.01305	0	14	82	17.07317	0.03088	0	25	199	12.56281	0.003541	0	48	681	7.048458	0.37250	238	721	33.009709	0.99890	1	116	12.2105263	0.37310	195	20.52632	0.4153	0	113	526.000	21.482890	0.893100	1	31	98.000	31.632653	0.9318	1	17	900	1.8888889	0.29830	713	950.000	75.05263	0.6900	0	694	17	2.449568	0.3163	0	0	0.000000	0.2466	0	7	199	3.5175879	0.39980	0	68	199.000	34.170854	0.9826	1	274	950	28.8421053	0.9832	1	2.315741	0.4476	2	2.911600	0.70420	2	0.6900	0.6839	0	2.92850	0.6794	2	8.845841	0.6188	6	Yes	\$0	0	0	\$0	0	Census Tract 1, Aleutians West Census Area, Alaska	26600	103800	33125	71500	30856.00	120408	2269.00	0.0735351	-48908	-0.4061856	NA	NA	NA	NA	NA
02020000300	02020	000300	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	6308	1834	1707	1137	5839	19.47251	0.4988	0	59	1024	5.761719	0.26830	0	11	11	100.00000	0.99780	1	609	1696	35.90802	0.17490	0	620	1707	36.32103	0.215100	0	85	2458	3.458096	0.12670	0	125	4961	2.519653	0.02643	0	0	0.000000	0.003301	2744	43.500317	0.99640	1	54	2007	2.690583	0.007821	0	301	1635	18.40979	0.6168	0	11	5308	0.2072344	0.06620	0	2167	6308	34.35320	0.3715	0	1834	24	1.308615	0.27080	0	0	0.0000000	0.2497	0	10	1707	0.5858231	0.1573	0	10	1707	0.5858231	0.07765	0	469	6308	7.4350032	0.9359	1	1.135330	0.1355	0	1.690522	0.13070	1	0.3715	0.3677	0	1.69135	0.1520	1	4.888702	0.1113	2	8256	1834	1731	1603	6583	24.35060	0.6772	0	95	1105	8.597285	0.8029	1	7	16	43.750000	0.91050	1	1127	1715	65.71429	0.88900	1	1134	1731	65.51127	0.985700	1	148	3181	4.652625	0.23830	80	5243	1.525844	0.08775	0	119	1.4413760	0.00975	3086	37.37888	0.9880	1	193	2171.088	8.889551	0.188800	0	136	1429.970	9.510687	0.3216	0	0	7040	0.0000000	0.02391	3808	8256.294	46.12239	0.4209	0	1834	127	6.924755	0.4701	0	0	0.000000	0.2466	0	13	1731	0.7510110	0.12710	0	179	1731.395	10.338487	0.7913	1	1673	8256	20.2640504	0.9768	1	2.791850	0.5891	2	1.532060	0.07776	1	0.4209	0.4172	0	2.61190	0.5330	2	7.356710	0.4139	5	Yes	\$0	0	0	\$0	0	Census Tract 3, Anchorage Municipality, Alaska	32404	NA	31620	NA	37588.64	NA	-5968.64	-0.1587884	NA	NA	NA	NA	Anchorage Municipality, Alaska	Anchorage, AK MSA	C1126
02020000400	02020	000400	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	5991	1360	1246	628	4602	13.64624	0.3404	0	117	924	12.662338	0.81630	1	0	12	0.00000	0.00240	0	761	1234	61.66937	0.78730	1	761	1246	61.07544	0.929600	1	24	1995	1.203008	0.03078	0	55	4075	1.349693	0.01061	0	0	0.000000	0.003301	2117	35.336338	0.93430	1	86	1820	4.725275	0.029420	0	138	1246	11.07544	0.3314	0	14	5099	0.2745636	0.07606	0	1539	5991	25.68853	0.2688	0	1360	0	0.000000	0.09395	0	10	0.7352941	0.5653	0	38	1246	3.0497592	0.4365	0	21	1246	1.6853933	0.19700	0	1389	5991	23.1847772	0.9762	1	2.127690	0.4021	2	1.374481	0.05613	1	0.2688	0.2660	0	2.26895	0.3836	1	6.039921	0.2480	4	5090	1440	1377	657	4243	15.48433	0.4416	0	82	1435	5.714286	0.5455	0	0	0	NaN	NA	NA	912	1377	66.23094	0.89700	1	912	1377	66.23094	0.987300	1	28	1928	1.452282	0.05471	82	3349	2.448492	0.16300	0	12	0.2357564	0.00585	1446	28.40864	0.8460	1	68	1902.717	3.573837	0.008563	0	56	1032.000	5.426357	0.1342	0	9	4411	0.2040354	0.06983	2444	5089.955	48.01614	0.4425	0	1440	38	2.638889	0.3255	0	0	0.000000	0.2466	0	7	1377	0.5083515	0.09514	0	92	1377.000	6.681191	0.6436	0	820	5090	16.1100196	0.9730	1	2.192110	0.4140	1	1.064443	0.02264	1	0.4425	0.4386	0	2.28384	0.3878	1	5.982893	0.2198	3	Yes	\$0	0	0	\$0	0	Census Tract 4, Anchorage Municipality, Alaska	23868	NA	30710	NA	27686.88	NA	3023.12	0.1091896	NA	NA	NA	NA	Anchorage Municipality, Alaska	Anchorage, AK MSA	C1126
02020000500	02020	000500	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	1872	979	956	384	1872	20.51282	0.5238	0	30	957	3.134796	0.06633	0	56	149	37.58389	0.39630	0	321	807	39.77695	0.23920	0	377	956	39.43515	0.311800	0	190	1139	16.681299	0.60890	0	314	2109	14.888573	0.48950	0	221	11.805556	0.574800	434	23.183761	0.43040	0	307	1475	20.813559	0.894900	1	91	385	23.63636	0.7603	1	129	1793	7.1946458	0.58420	0	1048	1872	55.98291	0.5787	0	979	578	59.039837	0.95260	1	0	0.0000000	0.2497	0	22	956	2.3012552	0.3729	0	78	956	8.1589958	0.68640	0	0	1872	0.0000000	0.3743	0	2.000330	0.3676	0	3.244600	0.82880	2	0.5787	0.5727	0	2.63590	0.5502	1	8.459530	0.5669	3	2039	1074	985	624	2039	30.60324	0.7906	1	119	1125	10.577778	0.8901	1	42	138	30.434783	0.56020	0	361	847	42.62102	0.32940	0	403	985	40.91371	0.614800	0	61	1468	4.155313	0.20970	350	1966	17.802645	0.95510	1	200	9.8087298	0.22920	322	15.79205	0.1707	0	233	1644.283	14.170309	0.581400	0	143	338.000	42.307692	0.9859	1	48	1920	2.5000000	0.35480	1060	2039.045	51.98512	0.4840	0	1074	642	59.776536	0.9485	1	0	0.000000	0.2466	0	39	985	3.9593909	0.43720	0	230	985.000	23.350254	0.9573	1	0	2039	0.0000000	0.1370	0	3.460300	0.7607	3	2.322000	0.38140	1	0.4840	0.4797	0	2.72660	0.5866	2	8.992900	0.6375	6	Yes	\$0	0	0	\$0	0	Census Tract 5, Anchorage Municipality, Alaska	28705	325000	29432	378600	33297.80	377000	-3865.80	-0.1160978	1600	0.0042440	149.51	185.49	Anchorage Municipality, Alaska	Anchorage, AK MSA	C1126
02020000701	02020	000701	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	5432	2076	1969	1206	5418	22.25914	0.5643	0	264	2765	9.547920	0.62650	0	354	1051	33.68221	0.26640	0	362	918	39.43355	0.23330	0	716	1969	36.36364	0.216100	0	411	3280	12.530488	0.50270	0	1108	5795	19.119931	0.64920	0	354	6.516937	0.200300	1479	27.227540	0.65230	0	567	4056	13.979290	0.607400	0	415	1255	33.06773	0.9178	1	73	4960	1.4717742	0.22780	0	3080	5432	56.70103	0.5848	0	2076	273	13.150289	0.63880	0	335	16.1368015	0.8980	1	166	1969	8.4306755	0.7014	0	202	1969	10.2590147	0.76450	1	0	5432	0.0000000	0.3743	0	2.558800	0.5224	0	2.605600	0.53860	1	0.5848	0.5788	0	3.37700	0.8627	2	9.126200	0.6476	3	6784	2585	2265	1300	6719	19.34812	0.5567	0	196	3597	5.448985	0.5123	0	356	1275	27.921569	0.45790	0	443	990	44.74747	0.37870	0	799	2265	35.27594	0.419800	0	363	3964	9.157417	0.46990	651	6607	9.853186	0.76060	1	437	6.4416274	0.06927	2252	33.19575	0.9548	1	945	4355.000	21.699196	0.897900	1	179	1612.000	11.104218	0.3936	0	481	6172	7.7932599	0.65010	4356	6784.000	64.20991	0.5963	0	2585	356	13.771760	0.6278	0	424	16.402321	0.9130	1	195	2265	8.6092715	0.68030	0	250	2265.000	11.037528	0.8145	1	7	6784	0.1031840	0.3090	0	2.719300	0.5684	1	2.965670	0.73150	2	0.5963	0.5911	0	3.34460	0.8443	2	9.625870	0.7156	5	Yes	\$0	0	0	\$0	0	Census Tract 7.01, Anchorage Municipality, Alaska	30261	212800	35306	230800	35102.76	246848	203.24	0.0057899	-16048	-0.0650117	196.20	222.97	Anchorage Municipality, Alaska	Anchorage, AK MSA	C1126
02020000702	02020	000702	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	5312	1972	1853	1154	5242	22.01450	0.5594	0	121	2647	4.571213	0.16150	0	229	784	29.20918	0.15100	0	418	1069	39.10196	0.22630	0	647	1853	34.91635	0.176500	0	172	2799	6.145052	0.25580	0	941	5126	18.357394	0.62460	0	202	3.802711	0.053090	1602	30.158133	0.78390	1	390	3602	10.827318	0.374000	0	279	1344	20.75893	0.6878	0	84	4700	1.7872340	0.26170	0	2129	5312	40.07907	0.4352	0	1972	250	12.677485	0.62970	0	48	2.4340771	0.6783	0	142	1853	7.6632488	0.6768	0	80	1853	4.3173233	0.45840	0	29	5312	0.5459337	0.7587	1	1.777800	0.3048	0	2.160490	0.31610	1	0.4352	0.4308	0	3.20190	0.8004	1	7.575390	0.4514	2	6391	2512	2317	1253	6298	19.89520	0.5724	0	101	2893	3.491186	0.2572	0	404	1230	32.845529	0.65310	0	524	1087	48.20607	0.46800	0	928	2317	40.05179	0.586300	0	431	3563	12.096548	0.57540	433	6087	7.113521	0.60170	0	634	9.9202003	0.23630	1892	29.60413	0.8823	1	1004	4195.000	23.933254	0.936100	1	351	1366.000	25.695461	0.8500	1	129	5974	2.1593572	0.32410	3992	6391.000	62.46284	0.5804	0	2512	548	21.815287	0.7513	1	200	7.961783	0.8180	1	220	2317	9.4950367	0.71030	0	120	2317.000	5.179111	0.5520	0	48	6391	0.7510562	0.6479	0	2.593000	0.5336	0	3.228800	0.84110	3	0.5804	0.5753	0	3.47950	0.8832	2	9.881700	0.7464	5	Yes	\$0	0	0	\$0	0	Census Tract 7.02, Anchorage Municipality, Alaska	30132	265800	27486	280300	34953.12	308328	-7467.12	-0.2136324	-28028	-0.0909032	192.20	212.03	Anchorage Municipality, Alaska	Anchorage, AK MSA	C1126
02020000703	02020	000703	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	5309	2312	2051	1208	5217	23.15507	0.5826	0	440	2750	16.000000	0.91910	1	269	929	28.95587	0.14470	0	613	1122	54.63458	0.61220	0	882	2051	43.00341	0.440600	0	480	3244	14.796548	0.56470	0	1399	5075	27.566502	0.85540	1	526	9.907704	0.446600	1355	25.522697	0.56000	0	1039	3732	27.840300	0.976800	1	304	1256	24.20382	0.7732	1	292	4943	5.9073437	0.53390	0	3146	5309	59.25786	0.6070	0	2312	514	22.231834	0.77020	1	235	10.1643599	0.8305	1	151	2051	7.3622623	0.6657	0	156	2051	7.6060458	0.66140	0	32	5309	0.6027500	0.7607	1	3.362400	0.7239	2	3.290500	0.84540	2	0.6070	0.6007	0	3.68850	0.9360	3	10.948400	0.8380	7	6007	2397	2191	1596	5859	27.24014	0.7337	0	231	3303	6.993642	0.6809	0	321	1072	29.944030	0.54100	0	571	1120	50.98214	0.54040	0	892	2192	40.69343	0.608200	0	293	3692	7.936078	0.41630	766	5779	13.254888	0.87670	1	739	12.3023140	0.37940	1707	28.41685	0.8466	1	625	4057.618	15.403126	0.662900	0	387	1378.067	28.082823	0.8878	1	126	5449	2.3123509	0.33780	3959	6006.510	65.91182	0.6115	0	2397	315	13.141427	0.6161	0	303	12.640801	0.8788	1	273	2191	12.4600639	0.78630	1	240	2191.439	10.951706	0.8114	1	201	6007	3.3460962	0.8922	1	3.315800	0.7254	1	3.114500	0.79680	2	0.6115	0.6061	0	3.98480	0.9711	4	11.026600	0.8725	7	Yes	\$0	0	0	\$0	0	Census Tract 7.03, Anchorage Municipality, Alaska	19589	147100	26957	194200	22723.24	170636	4233.76	0.1863185	23564	0.1380951	138.68	150.83	Anchorage Municipality, Alaska	Anchorage, AK MSA	C1126
02020000801	02020	000801	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	6878	2593	2380	1901	6821	27.86981	0.6792	0	514	3678	13.974986	0.86730	1	415	976	42.52049	0.57230	0	595	1404	42.37892	0.29620	0	1010	2380	42.43697	0.418100	0	383	3752	10.207889	0.42440	0	1608	7420	21.671159	0.72520	0	378	5.495784	0.132500	2001	29.092759	0.74220	0	879	4943	17.782723	0.804800	1	535	1648	32.46359	0.9101	1	328	6158	5.3264047	0.50640	0	4037	6878	58.69439	0.6022	0	2593	323	12.456614	0.62550	0	122	4.7049749	0.7403	0	246	2380	10.3361345	0.7506	1	250	2380	10.5042017	0.76960	1	127	6878	1.8464670	0.8242	1	3.114200	0.6623	1	3.096000	0.76840	2	0.6022	0.5960	0	3.71020	0.9397	3	10.522600	0.7937	6	8039	2575	2349	1877	7913	23.72046	0.6629	0	224	3708	6.040992	0.5815	0	364	1245	29.236948	0.51410	0	332	1104	30.07246	0.11540	0	696	2349	29.62963	0.234900	0	631	4356	14.485767	0.63730	1269	7749	16.376307	0.93800	1	569	7.0779948	0.09286	2750	34.20823	0.9664	1	925	5009.000	18.466760	0.806400	1	342	1360.000	25.147059	0.8398	1	178	7027	2.5330867	0.35840	6353	8039.000	79.02724	0.7276	0	2575	438	17.009709	0.6842	0	228	8.854369	0.8322	1	342	2349	14.5593870	0.82720	1	262	2349.000	11.153682	0.8169	1	101	8039	1.2563752	0.7476	0	3.054600	0.6607	1	3.063860	0.77520	3	0.7276	0.7212	0	3.90810	0.9647	3	10.754160	0.8443	7	Yes	\$0	0	0	\$0	0	Census Tract 8.01, Anchorage Municipality, Alaska	24433	214200	28895	217000	28342.28	248472	552.72	0.0195016	-31472	-0.1266622	175.52	189.05	Anchorage Municipality, Alaska	Anchorage, AK MSA	C1126
02020000802	02020	000802	AK	Alaska	Anchorage Municipality	4	West Region	9	Pacific Division	4412	1955	1860	927	4412	21.01088	0.5341	0	366	2358	15.521629	0.90830	1	329	993	33.13192	0.24940	0	427	867	49.25029	0.46580	0	756	1860	40.64516	0.351500	0	284	2541	11.176702	0.45970	0	944	4351	21.696162	0.72640	0	291	6.595648	0.206200	1116	25.294651	0.54610	0	456	3183	14.326107	0.630400	0	365	1034	35.29981	0.9397	1	147	4065	3.6162362	0.41080	0	2063	4412	46.75884	0.4982	0	1955	526	26.905371	0.81460	1	235	12.0204604	0.8530	1	101	1860	5.4301075	0.5881	0	144	1860	7.7419355	0.66870	0	0	4412	0.0000000	0.3743	0	2.980000	0.6306	1	2.733200	0.60110	1	0.4982	0.4931	0	3.29870	0.8370	2	9.510100	0.6929	4	4596	1925	1698	1372	4591	29.88456	0.7781	1	244	2652	9.200603	0.8347	1	173	857	20.186698	0.15430	0	314	841	37.33650	0.21580	0	487	1698	28.68080	0.205800	0	329	2968	11.084906	0.54380	851	4533	18.773439	0.96420	1	366	7.9634465	0.13400	1030	22.41079	0.5367	0	416	3503.000	11.875535	0.412100	0	383	1001.000	38.261738	0.9747	1	189	4158	4.5454545	0.50160	2768	4596.000	60.22628	0.5606	0	1925	621	32.259740	0.8439	1	175	9.090909	0.8354	1	129	1698	7.5971731	0.64450	0	66	1698.000	3.886926	0.4438	0	33	4596	0.7180157	0.6406	0	3.326600	0.7282	3	2.559100	0.51470	1	0.5606	0.5556	0	3.40820	0.8630	2	9.854500	0.7420	6	Yes	\$0	0	0	\$0	0	Census Tract 8.02, Anchorage Municipality, Alaska	26412	126100	30241	141400	30637.92	146276	-396.92	-0.0129552	-4876	-0.0333342	153.74	179.71	Anchorage Municipality, Alaska	Anchorage, AK MSA	C1126

svi_national_nmtc_df0 <- left_join(svi_national_nmtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_national_nmtc_df1 <- left_join(svi_national_nmtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_national_nmtc_df <- left_join(svi_national_nmtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_national_nmtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	F_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	nmtc_eligibility	pre10_nmtc_dollars_formatted	post10_nmtc_project_cnt	post10_nmtc_dollars	post10_nmtc_dollars_formatted	nmtc_flag	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
01001020200	01001	020200	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2020	816	730	495	1992	24.84940	0.5954	0	68	834	8.153477	0.57540	0	49	439	11.16173	0.02067	105	291	36.08247	0.30190	0	154	730	21.09589	0.09312	339	1265	26.798419	0.8392	1	313	2012	15.55666	0.6000	0	204	10.09901	0.3419	0	597	29.55446	0.8192	1	359	1515	23.69637	0.8791	1	132	456	28.947368	0.8351	1	15	1890	0.7936508	0.40130	0	1243	2020	61.534653	0.77810	1	816	0	0.0000000	0.1224	0	34	4.1666667	0.6664	0	13	730	1.780822	0.5406	0	115	730	15.7534247	0.83820	1	0	2020	0.0000	0.3640	0	2.70312	0.5665	1	3.27660	0.8614	3	0.77810	0.7709	1	2.53160	0.5047	1	9.28942	0.6832	6	1757	720	573	384	1511	25.413633	0.6427	0	29	717	4.044630	0.41320	0	33	392	8.418367	0.03542	0	116	181	64.08840	0.9086	1	149	573	26.00349	0.40410	0	139	1313	10.586443	0.5601	0	91	1533	5.936073	0.4343	0	284	16.163916	0.5169	0	325	18.49744	0.28510	0	164	1208.000	13.576159	0.4127	0	42	359.0000	11.6991643	0.39980	0	0	1651	0.0000000	0.09479	1116	1757.000	63.5173591	0.759100	1	720	3	0.4166667	0.2470	0	5	0.6944444	0.5106	0	9	573	1.5706806	0.46880	0	57	573.000	9.947644	0.7317	212	1757	12.0660216	0.9549	1	2.45440	0.4888	0	1.70929	0.10250	0	0.759100	0.752700	1	2.91300	0.6862	1	7.835790	0.4802	2	Yes	\$0	0	0	\$0	0	Census Tract 202, Autauga County, Alabama	19437	138500	20154	90500	22546.92	160660	-2392.92	-0.1061307	-70160	-0.4366986	123.78	123.37	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01001020700	01001	020700	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	2664	1254	1139	710	2664	26.65165	0.6328	0	29	1310	2.213741	0.05255	0	134	710	18.87324	0.13890	187	429	43.58974	0.47090	0	321	1139	28.18262	0.28130	396	1852	21.382289	0.7478	0	345	2878	11.98749	0.4459	0	389	14.60210	0.6417	0	599	22.48499	0.4007	0	510	2168	23.52399	0.8752	1	228	712	32.022472	0.8712	1	0	2480	0.0000000	0.09298	0	694	2664	26.051051	0.51380	0	1254	8	0.6379585	0.2931	0	460	36.6826156	0.9714	1	0	1139	0.000000	0.1238	0	125	1139	10.9745391	0.74770	0	0	2664	0.0000	0.3640	0	2.16035	0.4069	0	2.88178	0.6997	2	0.51380	0.5090	0	2.50000	0.4882	1	8.05593	0.5185	3	3562	1313	1248	1370	3528	38.832200	0.8512	1	128	1562	8.194622	0.79350	1	168	844	19.905213	0.44510	0	237	404	58.66337	0.8359	1	405	1248	32.45192	0.60420	0	396	2211	17.910448	0.7857	1	444	3547	12.517620	0.7758	1	355	9.966311	0.1800	0	954	26.78271	0.79230	1	629	2593.000	24.257617	0.8730	1	171	797.0000	21.4554580	0.71860	0	0	3211	0.0000000	0.09479	1009	3562.000	28.3267827	0.466800	0	1313	14	1.0662605	0.3165	0	443	33.7395278	0.9663	1	73	1248	5.8493590	0.82110	1	17	1248.000	1.362180	0.1554	112	3562	3.1443010	0.8514	1	3.81040	0.8569	4	2.65869	0.58470	2	0.466800	0.462900	0	3.11070	0.7714	3	10.046590	0.7851	9	Yes	\$0	0	0	\$0	0	Census Tract 207, Autauga County, Alabama	22114	93800	20934	82400	25652.24	108808	-4718.24	-0.1839309	-26408	-0.2427027	95.94	108.47	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01001021100	01001	021100	AL	Alabama	Autauga County	3	South Region	6	East South Central Division	3298	1502	1323	860	3298	26.07641	0.6211	0	297	1605	18.504673	0.94340	1	250	1016	24.60630	0.32070	74	307	24.10423	0.11920	0	324	1323	24.48980	0.17380	710	2231	31.824294	0.8976	1	654	3565	18.34502	0.7018	0	411	12.46210	0.5001	0	738	22.37720	0.3934	0	936	2861	32.71583	0.9807	1	138	825	16.727273	0.5715	0	9	3155	0.2852615	0.25010	0	1979	3298	60.006064	0.77030	1	1502	14	0.9320905	0.3234	0	659	43.8748336	0.9849	1	44	1323	3.325775	0.7062	0	137	1323	10.3552532	0.73130	0	0	3298	0.0000	0.3640	0	3.33770	0.7351	2	2.69580	0.6028	1	0.77030	0.7631	1	3.10980	0.7827	1	9.91360	0.7557	5	3499	1825	1462	1760	3499	50.300086	0.9396	1	42	966	4.347826	0.45390	0	426	1274	33.437991	0.85200	1	52	188	27.65957	0.1824	0	478	1462	32.69494	0.61110	0	422	2488	16.961415	0.7638	1	497	3499	14.204058	0.8246	1	853	24.378394	0.8688	1	808	23.09231	0.58290	0	908	2691.100	33.740844	0.9808	1	179	811.6985	22.0525243	0.73230	0	8	3248	0.2463054	0.26220	1986	3498.713	56.7637257	0.717500	0	1825	29	1.5890411	0.3551	0	576	31.5616438	0.9594	1	88	1462	6.0191518	0.82690	1	148	1461.993	10.123166	0.7364	38	3499	1.0860246	0.7013	0	3.59300	0.8073	3	3.42700	0.91560	2	0.717500	0.711400	0	3.57910	0.9216	2	11.316600	0.9150	7	Yes	\$0	0	0	\$0	0	Census Tract 211, Autauga County, Alabama	17997	74000	20620	88600	20876.52	85840	-256.52	-0.0122875	2760	0.0321528	134.13	145.41	Autauga County, Alabama	Montgomery-Alexander City, AL CSA	CS388
01003010200	01003	010200	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	2612	1220	1074	338	2605	12.97505	0.2907	0	44	1193	3.688181	0.14720	0	172	928	18.53448	0.13090	31	146	21.23288	0.09299	0	203	1074	18.90130	0.05657	455	1872	24.305556	0.8016	1	456	2730	16.70330	0.6445	0	401	15.35222	0.6847	0	563	21.55436	0.3406	0	410	2038	20.11776	0.7755	1	64	779	8.215661	0.2181	0	0	2510	0.0000000	0.09298	0	329	2612	12.595712	0.31130	0	1220	38	3.1147541	0.4648	0	385	31.5573770	0.9545	1	20	1074	1.862197	0.5509	0	43	1074	4.0037244	0.40880	0	0	2612	0.0000	0.3640	0	1.94057	0.3398	1	2.11188	0.2802	1	0.31130	0.3084	0	2.74300	0.6129	1	7.10675	0.3771	3	2928	1312	1176	884	2928	30.191257	0.7334	0	29	1459	1.987663	0.13560	0	71	830	8.554217	0.03726	0	134	346	38.72832	0.3964	0	205	1176	17.43197	0.12010	0	294	2052	14.327485	0.6940	0	219	2925	7.487179	0.5423	0	556	18.989071	0.6705	0	699	23.87295	0.63390	0	489	2226.455	21.963167	0.8122	1	191	783.8820	24.3659136	0.77990	1	0	2710	0.0000000	0.09479	398	2927.519	13.5951280	0.251100	0	1312	13	0.9908537	0.3111	0	400	30.4878049	0.9557	1	6	1176	0.5102041	0.25900	0	81	1176.202	6.886570	0.6115	7	2928	0.2390710	0.4961	0	2.22540	0.4183	0	2.99129	0.76340	2	0.251100	0.249000	0	2.63340	0.5496	1	8.101190	0.5207	3	Yes	\$0	1	408000	\$408,000	1	Census Tract 102, Baldwin County, Alabama	23862	103200	26085	136900	27679.92	119712	-1594.92	-0.0576201	17188	0.1435779	128.38	166.27	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003010500	01003	010500	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	4230	1779	1425	498	3443	14.46413	0.3337	0	166	1625	10.215385	0.71790	0	151	1069	14.12535	0.04638	196	356	55.05618	0.73830	0	347	1425	24.35088	0.17010	707	2945	24.006791	0.7967	1	528	4001	13.19670	0.5005	0	619	14.63357	0.6436	0	790	18.67612	0.1937	0	536	3096	17.31266	0.6572	0	165	920	17.934783	0.6102	0	20	4021	0.4973887	0.32320	0	754	4230	17.825059	0.40230	0	1779	97	5.4525014	0.5525	0	8	0.4496908	0.4600	0	63	1425	4.421053	0.7762	1	90	1425	6.3157895	0.56910	0	787	4230	18.6052	0.9649	1	2.51890	0.5121	1	2.42790	0.4539	0	0.40230	0.3986	0	3.32270	0.8628	2	8.67180	0.6054	3	5877	1975	1836	820	5244	15.636918	0.3902	0	90	2583	3.484321	0.33610	0	159	1345	11.821561	0.10530	0	139	491	28.30957	0.1924	0	298	1836	16.23094	0.09053	0	570	4248	13.418079	0.6669	0	353	5247	6.727654	0.4924	0	1109	18.870172	0.6645	0	1144	19.46571	0.34110	0	717	4102.545	17.476956	0.6332	0	103	1286.1180	8.0085961	0.23410	0	0	5639	0.0000000	0.09479	868	5877.481	14.7682323	0.270900	0	1975	26	1.3164557	0.3359	0	45	2.2784810	0.6271	0	9	1836	0.4901961	0.25400	0	116	1835.798	6.318779	0.5811	633	5877	10.7708014	0.9507	1	1.97613	0.3410	0	1.96769	0.19610	0	0.270900	0.268600	0	2.74880	0.6077	1	6.963520	0.3406	1	Yes	\$0	0	0	\$0	0	Census Tract 105, Baldwin County, Alabama	21585	121100	28301	148500	25038.60	140476	3262.40	0.1302948	8024	0.0571201	191.57	213.49	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003010600	01003	010600	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	3724	1440	1147	1973	3724	52.98067	0.9342	1	142	1439	9.867964	0.69680	0	235	688	34.15698	0.62950	187	459	40.74074	0.40290	0	422	1147	36.79163	0.55150	497	1876	26.492537	0.8354	1	511	3661	13.95794	0.5334	0	246	6.60580	0.1481	0	1256	33.72718	0.9305	1	496	2522	19.66693	0.7587	1	274	838	32.696897	0.8779	1	32	3479	0.9198045	0.42810	0	2606	3724	69.978518	0.81840	1	1440	21	1.4583333	0.3683	0	321	22.2916667	0.9036	1	97	1147	8.456844	0.8956	1	167	1147	14.5597210	0.82090	1	0	3724	0.0000	0.3640	0	3.55130	0.7859	2	3.14330	0.8145	3	0.81840	0.8108	1	3.35240	0.8725	3	10.86540	0.8550	9	4115	1534	1268	1676	3997	41.931449	0.8814	1	294	1809	16.252073	0.96740	1	341	814	41.891892	0.94320	1	204	454	44.93392	0.5438	0	545	1268	42.98107	0.83620	1	624	2425	25.731959	0.9002	1	994	4115	24.155529	0.9602	1	642	15.601458	0.4841	0	1126	27.36331	0.81750	1	568	2989.000	19.003011	0.7045	0	212	715.0000	29.6503497	0.85920	1	56	3825	1.4640523	0.53120	2715	4115.000	65.9781288	0.773200	1	1534	0	0.0000000	0.1079	0	529	34.4850065	0.9685	1	101	1268	7.9652997	0.87950	1	89	1268.000	7.018927	0.6184	17	4115	0.4131227	0.5707	0	4.54540	0.9754	5	3.39650	0.90810	2	0.773200	0.766700	1	3.14500	0.7858	2	11.860100	0.9520	10	Yes	\$0	1	8000000	\$8,000,000	1	Census Tract 106, Baldwin County, Alabama	17788	81600	16453	104700	20634.08	94656	-4181.08	-0.2026298	10044	0.1061105	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011000	01003	011000	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	3758	2012	1576	1053	3758	28.02022	0.6597	0	66	1707	3.866432	0.16250	0	293	1297	22.59059	0.25080	83	279	29.74910	0.19030	0	376	1576	23.85787	0.15710	744	2723	27.322806	0.8465	1	996	4137	24.07542	0.8462	1	713	18.97286	0.8429	1	804	21.39436	0.3306	0	763	3295	23.15630	0.8670	1	155	1145	13.537118	0.4538	0	50	3475	1.4388489	0.51460	0	516	3758	13.730708	0.33300	0	2012	0	0.0000000	0.1224	0	606	30.1192843	0.9484	1	42	1576	2.664975	0.6476	0	96	1576	6.0913706	0.55620	0	0	3758	0.0000	0.3640	0	2.67200	0.5579	2	3.00890	0.7581	2	0.33300	0.3299	0	2.63860	0.5614	1	8.65250	0.6030	5	4921	1979	1732	1539	4908	31.356968	0.7523	1	150	2105	7.125891	0.72850	0	214	1471	14.547927	0.20260	0	59	261	22.60536	0.1167	0	273	1732	15.76212	0.07981	0	936	3332	28.091237	0.9206	1	861	4921	17.496444	0.8930	1	1039	21.113595	0.7653	1	1183	24.03983	0.64410	0	585	3738.000	15.650080	0.5371	0	81	1151.0000	7.0373588	0.19000	0	101	4546	2.2217334	0.61440	1244	4921.000	25.2794148	0.427800	0	1979	0	0.0000000	0.1079	0	527	26.6296109	0.9393	1	83	1732	4.7921478	0.77460	1	151	1732.000	8.718245	0.6904	20	4921	0.4064215	0.5688	0	3.37421	0.7528	3	2.75090	0.63780	1	0.427800	0.424200	0	3.08100	0.7597	2	9.633910	0.7366	6	Yes	\$0	0	0	\$0	0	Census Tract 110, Baldwin County, Alabama	19340	126400	23679	158700	22434.40	146624	1244.60	0.0554773	12076	0.0823603	129.69	188.85	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011406	01003	011406	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	3317	6418	1307	583	3317	17.57612	0.4181	0	70	1789	3.912800	0.16690	0	221	685	32.26277	0.57540	284	622	45.65916	0.52130	0	505	1307	38.63810	0.60430	168	2255	7.450111	0.2800	0	919	3677	24.99320	0.8623	1	452	13.62677	0.5791	0	673	20.28942	0.2668	0	366	2769	13.21777	0.4276	0	96	887	10.822999	0.3359	0	180	3066	5.8708415	0.77920	1	473	3317	14.259873	0.34330	0	6418	3976	61.9507635	0.9655	1	384	5.9831723	0.7063	0	17	1307	1.300689	0.4632	0	10	1307	0.7651109	0.08684	0	0	3317	0.0000	0.3640	0	2.33160	0.4577	1	2.38860	0.4323	1	0.34330	0.3401	0	2.58584	0.5335	1	7.64934	0.4576	3	3226	7850	1797	228	3215	7.091757	0.1241	0	72	2055	3.503650	0.33910	0	302	1139	26.514486	0.69300	0	230	658	34.95441	0.3131	0	532	1797	29.60490	0.52020	0	128	2726	4.695525	0.2384	0	530	3226	16.429014	0.8749	1	790	24.488531	0.8715	1	342	10.60136	0.05624	0	280	2884.000	9.708738	0.1832	0	58	792.0000	7.3232323	0.20270	0	15	3107	0.4827808	0.34070	15	3226.000	0.4649721	0.002512	0	7850	5394	68.7133758	0.9706	1	274	3.4904459	0.6697	0	23	1797	1.2799110	0.41980	0	26	1797.000	1.446856	0.1647	0	3226	0.0000000	0.1831	0	2.09670	0.3785	1	1.65434	0.08785	1	0.002512	0.002491	0	2.40790	0.4381	1	6.161452	0.2215	3	Yes	\$0	0	0	\$0	0	Census Tract 114.06, Baldwin County, Alabama	29838	252000	32201	224200	34612.08	292320	-2411.08	-0.0696601	-68120	-0.2330323	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011407	01003	011407	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	5187	6687	2066	1404	5172	27.14617	0.6423	0	172	1935	8.888889	0.63280	0	482	1433	33.63573	0.61530	367	633	57.97788	0.79510	1	849	2066	41.09390	0.67110	278	3618	7.683803	0.2906	0	1027	4945	20.76845	0.7735	1	1398	26.95200	0.9629	1	1263	24.34933	0.5302	0	596	3792	15.71730	0.5759	0	158	1633	9.675444	0.2833	0	29	4867	0.5958496	0.35240	0	170	5187	3.277424	0.07984	0	6687	2772	41.4535666	0.9251	1	197	2.9460147	0.6326	0	90	2066	4.356244	0.7729	1	0	2066	0.0000000	0.02586	0	0	5187	0.0000	0.3640	0	3.01030	0.6516	1	2.70470	0.6077	1	0.07984	0.0791	0	2.72046	0.6014	2	8.51530	0.5852	4	5608	7576	2543	1058	5602	18.886112	0.4835	0	32	2631	1.216268	0.05882	0	581	1979	29.358262	0.77080	1	309	564	54.78723	0.7671	1	890	2543	34.99803	0.67250	0	230	4433	5.188360	0.2698	0	776	5602	13.852196	0.8156	1	1527	27.228959	0.9205	1	567	10.11056	0.05099	0	615	5035.000	12.214498	0.3295	0	16	1746.0000	0.9163803	0.01566	0	0	5573	0.0000000	0.09479	441	5608.000	7.8637660	0.140300	0	7576	3055	40.3247096	0.9148	1	72	0.9503696	0.5383	0	0	2543	0.0000000	0.09796	0	125	2543.000	4.915454	0.4934	6	5608	0.1069900	0.4054	0	2.30022	0.4418	1	1.41144	0.04295	1	0.140300	0.139100	0	2.44986	0.4589	1	6.301820	0.2416	3	Yes	\$0	0	0	\$0	0	Census Tract 114.07, Baldwin County, Alabama	22317	292600	28418	241100	25887.72	339416	2530.28	0.0977406	-98316	-0.2896622	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380
01003011502	01003	011502	AL	Alabama	Baldwin County	3	South Region	6	East South Central Division	9234	4606	3702	3160	9213	34.29936	0.7632	1	282	4002	7.046477	0.47570	0	526	2158	24.37442	0.31260	582	1544	37.69430	0.33410	0	1108	3702	29.92977	0.33740	997	6176	16.143135	0.6201	0	2074	10111	20.51231	0.7670	1	1450	15.70284	0.7043	0	2491	26.97639	0.6984	0	1542	7577	20.35106	0.7842	1	684	2718	25.165563	0.7767	1	532	8697	6.1170519	0.78590	1	3275	9234	35.466753	0.60970	0	4606	214	4.6461138	0.5268	0	828	17.9765523	0.8689	1	89	3702	2.404106	0.6192	0	293	3702	7.9146407	0.64700	0	0	9234	0.0000	0.3640	0	2.96340	0.6387	2	3.74950	0.9623	3	0.60970	0.6040	0	3.02590	0.7475	1	10.34850	0.8024	6	14165	6867	6002	2853	14165	20.141193	0.5175	0	313	7047	4.441606	0.46620	0	1181	4164	28.362152	0.74500	0	887	1838	48.25898	0.6211	0	2068	6002	34.45518	0.65900	0	1667	10750	15.506977	0.7286	0	2527	14165	17.839746	0.8980	1	3082	21.757854	0.7907	1	2506	17.69149	0.24240	0	3004	11659.000	25.765503	0.9038	1	407	3482.0000	11.6886847	0.39940	0	364	13519	2.6925068	0.65290	2755	14165.000	19.4493470	0.346300	0	6867	441	6.4220183	0.5555	0	526	7.6598223	0.7585	1	93	6002	1.5494835	0.46540	0	184	6002.000	3.065645	0.3373	0	14165	0.0000000	0.1831	0	3.26930	0.7261	1	2.98920	0.76250	2	0.346300	0.343400	0	2.29980	0.3856	1	8.904600	0.6398	4	Yes	\$0	2	8860000	\$8,860,000	1	Census Tract 115.02, Baldwin County, Alabama	20411	162700	22820	180400	23676.76	188732	-856.76	-0.0361857	-8332	-0.0441473	NA	NA	Baldwin County, Alabama	Mobile-Daphne-Fairhope, AL CSA	CS380

LIHTC Data

svi_divisional_lihtc_df0 <- left_join(svi_divisional_lihtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_divisional_lihtc_df1 <- left_join(svi_divisional_lihtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_divisional_lihtc_df <- left_join(svi_divisional_lihtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_divisional_lihtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	F_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	F_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	F_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	post10_lihtc_project_cnt	post10_lihtc_project_dollars	lihtc_flag	lihtc_eligibility	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
02050000100	02050	000100	AK	Alaska	Bethel Census Area	4	West Region	9	Pacific Division	9481	2776	2127	4499	9422	47.74995	0.9162	1	923	3537	26.095561	0.9936	1	224	1570	14.26752	0.010260	0	35	557	6.283663	0.012980	259	2127	12.17677	0.003169	0	1431	4685	30.544290	0.8055	1	2901	9557	30.354714	0.89790	1	688	7.256619	0.24940	3678	38.79338	0.97710	1	1085	5745	18.88599	0.8446	1	418	1677	24.92546	0.7894	1	771	8382	9.1982820	0.64930	9146	9481	96.46662	0.9412	1	2776	3	0.1080692	0.18850	0	14	0.5043228	0.5274	992	2127	46.638458	0.99440	1	1814	2127	85.28444	0.9993	1	0	9481	0.000000	0.3743	0	3.616369	0.7794	4	3.50980	0.9107	3	0.9412	0.9315	1	3.08390	0.7535	2	11.15127	0.8587	10	10311	2692	2104	5779	10267	56.28713	0.9839	1	870	3667	23.725116	0.9967	1	232	1494	15.52878	0.05333	0	95	610	15.57377	0.02547	327	2104	15.54183	0.009597	1228	5181	23.701988	0.78920	1	1639	10294	15.921896	0.9319	1	812	7.875085	0.12960	4008	38.87111	0.99260	1	1259	6286.0000	20.028635	0.85600	1	483	1769.0000	27.30356	0.8759	1	188	9020	2.0842572	0.31690	10181	10311.000	98.73921	0.9760	1	2692	1	0.0371471	0.16590	0	31	1.1515602	0.6200	0	1024	2104	48.669201	0.9978	1	1793	2104.0000	85.218631	0.9993	1	477	10311	4.6261274	0.9233	1	3.711297	0.8199	4	3.17100	0.82060	3	0.9760	0.9674	1	3.70630	0.9326	3	11.56460	0.9233	11	0	0	0	Yes	Census Tract 1, Bethel Census Area, Alaska	10580	106600	10671	52100	12272.80	123656	-1601.80	-0.1305163	-71556	-0.5786699	NA	NA	NA	NA	NA
02050000300	02050	000300	AK	Alaska	Bethel Census Area	4	West Region	9	Pacific Division	1386	725	439	460	1383	33.26103	0.7628	1	118	596	19.798658	0.9694	1	38	308	12.33766	0.008283	0	10	131	7.633588	0.014190	48	439	10.93394	0.002703	0	168	777	21.621622	0.7013	0	477	1475	32.338983	0.92130	1	160	11.544011	0.55680	464	33.47763	0.89380	1	122	955	12.77487	0.5244	0	99	318	31.13208	0.8947	1	4	1284	0.3115265	0.08126	1161	1386	83.76623	0.8084	1	725	0	0.0000000	0.09395	0	8	1.1034483	0.6032	90	439	20.501139	0.89560	1	261	439	59.45330	0.9957	1	0	1386	0.000000	0.3743	0	3.357503	0.7224	3	2.95096	0.7007	2	0.8084	0.8000	1	2.96275	0.7006	2	10.07961	0.7498	8	1404	742	369	597	1379	43.29224	0.9267	1	152	646	23.529412	0.9965	1	50	267	18.72659	0.11360	0	27	102	26.47059	0.08218	77	369	20.86721	0.046030	149	794	18.765743	0.71930	0	345	1404	24.572650	0.9915	1	115	8.190883	0.14420	484	34.47293	0.96900	1	139	920.0005	15.108688	0.64470	0	89	276.0002	32.24635	0.9371	1	6	1243	0.4827031	0.11630	1240	1404.000	88.31906	0.8327	1	742	0	0.0000000	0.08271	0	9	1.2129380	0.6256	0	112	369	30.352304	0.9725	1	223	369.0005	60.433530	0.9961	1	94	1404	6.6951567	0.9478	1	3.680030	0.8126	3	2.81130	0.65190	2	0.8327	0.8253	1	3.62471	0.9189	3	10.94874	0.8637	9	0	0	0	Yes	Census Tract 3, Bethel Census Area, Alaska	14778	197900	14646	160900	17142.48	229564	-2496.48	-0.1456312	-68664	-0.2991061	NA	NA	NA	NA	NA
02070000100	02070	000100	AK	Alaska	Dillingham Census Area	4	West Region	9	Pacific Division	2569	1354	584	1037	2565	40.42885	0.8513	1	236	853	27.667057	0.9954	1	68	398	17.08543	0.016280	0	34	186	18.279570	0.034550	102	584	17.46575	0.006339	0	384	1303	29.470453	0.7955	1	1140	2710	42.066421	0.98140	1	217	8.446867	0.33900	940	36.59011	0.95310	1	311	1728	17.99769	0.8126	1	94	442	21.26697	0.7005	0	203	2363	8.5907744	0.63010	2410	2569	93.81082	0.9081	1	1354	0	0.0000000	0.09395	0	14	1.0339734	0.5974	186	584	31.849315	0.96500	1	367	584	62.84247	0.9966	1	0	2569	0.000000	0.3743	0	3.629939	0.7830	4	3.43530	0.8919	2	0.9081	0.8988	1	3.02725	0.7274	2	11.00059	0.8430	9	2801	1444	718	1191	2792	42.65759	0.9224	1	183	1059	17.280453	0.9849	1	94	487	19.30185	0.12840	0	51	231	22.07792	0.05382	145	718	20.19499	0.039410	265	1619	16.368129	0.67640	0	552	2801	19.707247	0.9721	1	353	12.602642	0.39670	862	30.77472	0.91140	1	295	1939.1327	15.212986	0.65170	0	200	579.0000	34.54231	0.9555	1	49	2513	1.9498607	0.30380	2536	2801.124	90.53509	0.8619	1	1444	1	0.0692521	0.16740	0	10	0.6925208	0.5747	0	255	718	35.515320	0.9868	1	481	718.0000	66.991643	0.9972	1	230	2801	8.2113531	0.9566	1	3.595210	0.7924	3	3.21910	0.83820	2	0.8619	0.8543	1	3.68270	0.9288	3	11.35891	0.9048	9	0	0	0	Yes	Census Tract 1, Dillingham Census Area, Alaska	10750	113500	17367	85900	12470.00	131660	4897.00	0.3927025	-45760	-0.3475619	NA	NA	NA	NA	NA
02122000100	02122	000100	AK	Alaska	Kenai Peninsula Borough	4	West Region	9	Pacific Division	251	428	138	90	251	35.85657	0.7982	1	29	145	20.000000	0.9707	1	54	90	60.00000	0.930300	1	0	48	0.000000	0.005509	54	138	39.13043	0.301700	0	21	186	11.290323	0.4631	0	198	460	43.043478	0.98470	1	6	2.390438	0.02129	61	24.30279	0.49430	0	56	395	14.17722	0.6201	0	18	57	31.57895	0.8999	1	0	233	0.0000000	0.02799	205	251	81.67331	0.7907	1	428	0	0.0000000	0.09395	0	20	4.6728972	0.7396	7	138	5.072464	0.57090	0	17	138	12.31884	0.8207	1	0	251	0.000000	0.3743	0	3.518400	0.7575	3	2.06358	0.2722	1	0.7907	0.7826	1	2.59945	0.5334	1	8.97213	0.6292	6	531	307	131	193	523	36.90249	0.8743	1	74	324	22.839506	0.9958	1	23	92	25.00000	0.32780	0	4	39	10.25641	0.01129	27	131	20.61069	0.043330	6	389	1.542417	0.05899	0	220	523	42.065010	0.9998	1	12	2.259887	0.01198	111	20.90395	0.43940	0	50	412.0000	12.135924	0.43280	0	23	72.0000	31.94445	0.9342	1	0	512	0.0000000	0.02391	437	531.000	82.29756	0.7611	1	307	0	0.0000000	0.08271	0	16	5.2117264	0.7700	1	11	131	8.396947	0.6735	0	42	131.0000	32.061070	0.9796	1	111	531	20.9039548	0.9772	1	2.972220	0.6420	3	1.84229	0.16030	1	0.7611	0.7544	1	3.48301	0.8841	3	9.05862	0.6447	8	0	0	0	Yes	Census Tract 1, Kenai Peninsula Borough, Alaska	22885	515600	NA	32200	26546.60	598096	NA	NA	-565896	-0.9461625	NA	NA	NA	NA	NA
02180000100	02180	000100	AK	Alaska	Nome Census Area	4	West Region	9	Pacific Division	5766	2016	1373	3052	5552	54.97118	0.9552	1	519	2134	24.320525	0.9899	1	224	852	26.29108	0.095960	0	94	521	18.042227	0.033620	318	1373	23.16096	0.021070	0	580	2709	21.410114	0.6970	0	1988	5811	34.210979	0.93800	1	299	5.185571	0.11630	2214	38.39750	0.97400	1	580	3550	16.33803	0.7460	0	439	1083	40.53555	0.9715	1	95	5090	1.8664047	0.26950	5430	5766	94.17274	0.9125	1	2016	15	0.7440476	0.22730	0	27	1.3392857	0.6231	495	1373	36.052440	0.97870	1	1167	1373	84.99636	0.9991	1	187	5766	3.243149	0.8747	1	3.601170	0.7768	3	3.07730	0.7608	2	0.9125	0.9031	1	3.70290	0.9385	3	11.29387	0.8751	9	5901	2111	1441	2939	5789	50.76870	0.9667	1	554	2224	24.910072	0.9980	1	237	1047	22.63610	0.23610	0	56	394	14.21320	0.02099	293	1441	20.33310	0.040350	586	2969	19.737285	0.73470	0	1202	5852	20.539986	0.9780	1	469	7.947806	0.13320	2245	38.04440	0.99060	1	590	3606.9999	16.357084	0.71430	0	532	1175.0000	45.27660	0.9916	1	161	5296	3.0400302	0.39890	5578	5901.000	94.52635	0.9154	1	2111	6	0.2842255	0.17600	0	23	1.0895310	0.6155	0	602	1441	41.776544	0.9943	1	1240	1441.0000	86.051353	0.9993	1	351	5901	5.9481444	0.9413	1	3.717750	0.8217	3	3.22860	0.84100	2	0.9154	0.9073	1	3.72640	0.9367	3	11.58815	0.9255	9	0	0	0	Yes	Census Tract 1, Nome Census Area, Alaska	11287	103000	15051	88100	13092.92	119480	1958.08	0.1495526	-31380	-0.2626381	NA	NA	NA	NA	NA
02290000100	02290	000100	AK	Alaska	Yukon-Koyukuk Census Area	4	West Region	9	Pacific Division	1127	969	515	482	1127	42.76841	0.8749	1	165	551	29.945553	0.9970	1	104	386	26.94301	0.106600	0	16	129	12.403101	0.019980	120	515	23.30097	0.022180	0	216	727	29.711142	0.7981	1	492	1121	43.889384	0.98650	1	65	5.767524	0.14900	329	29.19255	0.74620	0	193	825	23.39394	0.9394	1	85	247	34.41296	0.9316	1	13	1049	1.2392755	0.20170	960	1127	85.18190	0.8206	1	969	0	0.0000000	0.09395	0	30	3.0959752	0.7027	83	515	16.116505	0.84800	1	333	515	64.66019	0.9969	1	0	1127	0.000000	0.3743	0	3.678680	0.7918	4	2.96790	0.7088	2	0.8206	0.8122	1	3.01585	0.7215	2	10.48303	0.7894	9	1118	1030	445	516	1097	47.03737	0.9495	1	94	463	20.302376	0.9929	1	68	346	19.65318	0.13910	0	22	99	22.22222	0.05448	90	445	20.22472	0.039600	125	703	17.780939	0.70070	0	161	1099	14.649681	0.9100	1	159	14.221825	0.49590	338	30.23256	0.89890	1	158	761.0000	20.762155	0.87640	1	88	218.0000	40.36697	0.9809	1	0	1038	0.0000000	0.02391	1001	1118.000	89.53488	0.8480	1	1030	0	0.0000000	0.08271	0	17	1.6504854	0.6556	0	76	445	17.078652	0.8684	1	274	445.0000	61.573034	0.9965	1	58	1118	5.1878354	0.9330	1	3.592700	0.7918	3	3.27601	0.85820	3	0.8480	0.8405	1	3.53621	0.8979	3	11.25292	0.8955	10	0	0	0	Yes	Census Tract 1, Yukon-Koyukuk Census Area, Alaska	14127	91000	16500	88100	16387.32	105560	112.68	0.0068760	-17460	-0.1654036	NA	NA	NA	NA	NA
02290000400	02290	000400	AK	Alaska	Yukon-Koyukuk Census Area	4	West Region	9	Pacific Division	1173	751	377	573	1156	49.56747	0.9275	1	106	509	20.825147	0.9770	1	35	228	15.35088	0.012330	0	26	149	17.449664	0.031190	61	377	16.18037	0.005034	0	114	646	17.647059	0.6307	0	419	1005	41.691542	0.97960	1	101	8.610401	0.35070	362	30.86104	0.81090	1	109	696	15.66092	0.7109	0	80	230	34.78261	0.9347	1	0	1039	0.0000000	0.02799	995	1173	84.82523	0.8172	1	751	0	0.0000000	0.09395	0	18	2.3968043	0.6773	53	377	14.058355	0.81990	1	208	377	55.17241	0.9947	1	0	1173	0.000000	0.3743	0	3.519834	0.7583	3	2.83519	0.6497	2	0.8172	0.8088	1	2.96015	0.6987	2	10.13237	0.7552	8	1035	823	394	442	1030	42.91262	0.9240	1	76	481	15.800416	0.9754	1	66	292	22.60274	0.23430	0	21	102	20.58824	0.04701	87	394	22.08122	0.063080	88	653	13.476263	0.61280	0	291	1032	28.197674	0.9971	1	141	13.623188	0.45900	295	28.50242	0.84980	1	184	736.9996	24.966091	0.94930	1	71	214.9998	33.02329	0.9430	1	2	949	0.2107482	0.07122	904	1035.000	87.34303	0.8185	1	823	2	0.2430134	0.17400	0	3	0.3645200	0.5127	0	41	394	10.406091	0.7362	0	259	393.9995	65.736117	0.9969	1	24	1035	2.3188406	0.8486	1	3.572380	0.7884	3	3.27232	0.85640	3	0.8185	0.8113	1	3.26840	0.8181	2	10.93160	0.8621	9	0	0	0	Yes	Census Tract 4, Yukon-Koyukuk Census Area, Alaska	14207	92900	15492	55600	16480.12	107764	-988.12	-0.0599583	-52164	-0.4840578	NA	NA	NA	NA	NA
06001400700	06001	400700	CA	California	Alameda County	4	West Region	9	Pacific Division	3942	2009	1706	1186	3942	30.08625	0.7161	0	191	1969	9.700356	0.6400	0	298	607	49.09390	0.760800	1	658	1099	59.872611	0.748900	956	1706	56.03751	0.847400	1	380	2787	13.634733	0.5333	0	546	3779	14.448267	0.47150	0	451	11.440893	0.55060	692	17.55454	0.17100	0	707	3177	22.25370	0.9237	1	186	688	27.03488	0.8296	1	100	3664	2.7292576	0.34520	2542	3942	64.48503	0.6485	0	2009	78	3.8825286	0.39170	0	0	0.0000000	0.2497	25	1706	1.465416	0.27860	0	329	1706	19.28488	0.9224	1	0	3942	0.000000	0.3743	0	3.208300	0.6849	1	2.82010	0.6426	2	0.6485	0.6419	0	2.21670	0.3596	1	8.89360	0.6187	4	5127	2037	1926	1155	5110	22.60274	0.6384	0	209	3522	5.934128	0.5700	0	248	677	36.63220	0.77480	1	442	1248	35.41667	0.18340	690	1925	35.84416	0.439200	200	3980	5.025126	0.26040	0	462	5123	9.018153	0.7215	0	478	9.323191	0.20110	957	18.66589	0.30330	0	573	4166.1857	13.753588	0.55250	0	224	873.8465	25.63379	0.8488	1	188	4819	3.9012243	0.46260	2892	5126.788	56.40959	0.5263	0	2037	177	8.6892489	0.51730	0	0	0.0000000	0.2466	0	109	1926	5.659398	0.5535	0	249	1925.6234	12.930878	0.8598	1	21	5127	0.4095963	0.5222	0	2.629500	0.5445	0	2.36830	0.40540	1	0.5263	0.5217	0	2.69940	0.5738	1	8.22350	0.5369	2	0	0	0	Yes	Census Tract 4007, Alameda County, California	25303	453500	38235	702500	29351.48	526060	8883.52	0.3026600	176440	0.3353990	350.20	784.95	Alameda County, California	San Jose-San Francisco-Oakland, CA CSA	CS488
06001400900	06001	400900	CA	California	Alameda County	4	West Region	9	Pacific Division	2466	1196	1123	405	2466	16.42336	0.4185	0	101	1484	6.805930	0.3730	0	270	439	61.50342	0.943300	1	323	684	47.222222	0.410700	593	1123	52.80499	0.772400	1	240	1699	14.125956	0.5464	0	100	2275	4.395604	0.07064	0	258	10.462287	0.48400	439	17.80211	0.17720	0	409	1868	21.89507	0.9182	1	202	400	50.50000	0.9951	1	0	2321	0.0000000	0.02799	1495	2466	60.62449	0.6182	0	1196	80	6.6889632	0.48380	0	0	0.0000000	0.2497	0	1123	0.000000	0.05961	0	204	1123	18.16563	0.9113	1	0	2466	0.000000	0.3743	0	2.180940	0.4177	1	2.60249	0.5376	2	0.6182	0.6119	0	2.07871	0.2993	1	7.48034	0.4369	4	2854	1221	1101	410	2854	14.36580	0.4046	0	104	2043	5.090553	0.4664	0	155	404	38.36634	0.82100	1	309	697	44.33286	0.36900	464	1101	42.14351	0.653600	163	2053	7.939601	0.41660	0	197	2849	6.914707	0.5867	0	203	7.112824	0.09481	462	16.18781	0.18410	0	199	2387.8971	8.333693	0.15390	0	142	585.5745	24.24969	0.8226	1	181	2663	6.7968457	0.60790	1875	2854.008	65.69709	0.6092	0	1221	152	12.4488124	0.60240	0	0	0.0000000	0.2466	0	49	1101	4.450500	0.4765	0	65	1101.4226	5.901459	0.6007	0	0	2854	0.0000000	0.1370	0	2.527900	0.5132	0	1.86331	0.16880	1	0.6092	0.6038	0	2.06320	0.2946	0	7.06361	0.3731	1	0	0	0	Yes	Census Tract 4009, Alameda County, California	30185	470500	38375	774700	35014.60	545780	3360.40	0.0959714	228920	0.4194364	281.27	777.62	Alameda County, California	San Jose-San Francisco-Oakland, CA CSA	CS488
06001401100	06001	401100	CA	California	Alameda County	4	West Region	9	Pacific Division	3738	2218	2014	1244	3738	33.27983	0.7632	1	193	2527	7.637515	0.4589	0	230	400	57.50000	0.905100	1	927	1614	57.434944	0.685300	1157	2014	57.44786	0.875900	1	275	2920	9.417808	0.3945	0	716	4313	16.600974	0.55910	0	237	6.340289	0.18740	420	11.23596	0.06016	0	392	3734	10.49813	0.3470	0	123	535	22.99065	0.7451	0	367	3574	10.2686066	0.67930	1979	3738	52.94275	0.5530	0	2218	531	23.9404869	0.78650	1	0	0.0000000	0.2497	7	2014	0.347567	0.12930	0	567	2014	28.15293	0.9667	1	0	3738	0.000000	0.3743	0	3.051600	0.6495	2	2.01896	0.2542	0	0.5530	0.5473	0	2.50650	0.4877	2	8.13006	0.5257	4	4283	2222	2187	876	4283	20.45295	0.5868	0	194	3177	6.106390	0.5900	0	90	406	22.16749	0.21890	0	755	1781	42.39191	0.32490	845	2187	38.63740	0.538200	175	3482	5.025847	0.26060	0	454	4283	10.600047	0.7917	1	268	6.257296	0.06482	526	12.28111	0.08534	0	264	3757.5978	7.025765	0.08703	0	155	591.6622	26.19738	0.8596	1	114	4119	2.7676621	0.37920	2054	4283.310	47.95357	0.4417	0	2222	991	44.5994599	0.90590	1	0	0.0000000	0.2466	0	60	2187	2.743484	0.3343	0	553	2186.6359	25.289990	0.9637	1	114	4283	2.6616857	0.8651	1	2.767300	0.5836	1	1.47599	0.06543	1	0.4417	0.4378	0	3.31560	0.8335	3	8.00059	0.5035	5	1	1590984	1	Yes	Census Tract 4011, Alameda County, California	23516	526800	48058	889800	27278.56	611088	20779.44	0.7617499	278712	0.4560914	530.92	1038.24	Alameda County, California	San Jose-San Francisco-Oakland, CA CSA	CS488

svi_national_lihtc_df0 <- left_join(svi_national_lihtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_national_lihtc_df1 <- left_join(svi_national_lihtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_national_lihtc_df <- left_join(svi_national_lihtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_national_lihtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")

GEOID_2010_trt	county_fips	FIPS_tract	state	state_name	county	region_number	region	division_number	division	E_TOTPOP_10	E_HU_10	E_HH_10	E_POV150_10	ET_POVSTATUS_10	EP_POV150_10	EPL_POV150_10	F_POV150_10	E_UNEMP_10	ET_EMPSTATUS_10	EP_UNEMP_10	EPL_UNEMP_10	F_UNEMP_10	E_HBURD_OWN_10	ET_HOUSINGCOST_OWN_10	EP_HBURD_OWN_10	EPL_HBURD_OWN_10	F_HBURD_OWN_10	E_HBURD_RENT_10	ET_HOUSINGCOST_RENT_10	EP_HBURD_RENT_10	EPL_HBURD_RENT_10	F_HBURD_RENT_10	E_HBURD_10	ET_HOUSINGCOST_10	EP_HBURD_10	EPL_HBURD_10	F_HBURD_10	E_NOHSDP_10	ET_EDSTATUS_10	EP_NOHSDP_10	EPL_NOHSDP_10	F_NOHSDP_10	E_UNINSUR_12	ET_INSURSTATUS_12	EP_UNINSUR_12	EPL_UNINSUR_12	F_UNINSUR_12	E_AGE65_10	EP_AGE65_10	EPL_AGE65_10	F_AGE65_10	E_AGE17_10	EP_AGE17_10	EPL_AGE17_10	E_DISABL_12	ET_DISABLSTATUS_12	EP_DISABL_12	EPL_DISABL_12	F_DISABL_12	E_SNGPNT_10	ET_FAMILIES_10	EP_SNGPNT_10	EPL_SNGPNT_10	F_SNGPNT_10	E_LIMENG_10	ET_POPAGE5UP_10	EP_LIMENG_10	EPL_LIMENG_10	E_MINRTY_10	ET_POPETHRACE_10	EP_MINRTY_10	EPL_MINRTY_10	F_MINRTY_10	E_STRHU_10	E_MUNIT_10	EP_MUNIT_10	EPL_MUNIT_10	F_MUNIT_10	E_MOBILE_10	EP_MOBILE_10	EPL_MOBILE_10	F_MOBILE_10	E_CROWD_10	ET_OCCUPANTS_10	EP_CROWD_10	EPL_CROWD_10	F_CROWD_10	E_NOVEH_10	ET_KNOWNVEH_10	EP_NOVEH_10	EPL_NOVEH_10	F_NOVEH_10	E_GROUPQ_10	ET_HHTYPE_10	EP_GROUPQ_10	EPL_GROUPQ_10	F_GROUPQ_10	SPL_THEME1_10	RPL_THEME1_10	F_THEME1_10	SPL_THEME2_10	RPL_THEME2_10	F_THEME2_10	SPL_THEME3_10	RPL_THEME3_10	F_THEME3_10	SPL_THEME4_10	RPL_THEME4_10	F_THEME4_10	SPL_THEMES_10	RPL_THEMES_10	F_TOTAL_10	E_TOTPOP_20	E_HU_20	E_HH_20	E_POV150_20	ET_POVSTATUS_20	EP_POV150_20	EPL_POV150_20	F_POV150_20	E_UNEMP_20	ET_EMPSTATUS_20	EP_UNEMP_20	EPL_UNEMP_20	F_UNEMP_20	E_HBURD_OWN_20	ET_HOUSINGCOST_OWN_20	EP_HBURD_OWN_20	EPL_HBURD_OWN_20	F_HBURD_OWN_20	E_HBURD_RENT_20	ET_HOUSINGCOST_RENT_20	EP_HBURD_RENT_20	EPL_HBURD_RENT_20	F_HBURD_RENT_20	E_HBURD_20	ET_HOUSINGCOST_20	EP_HBURD_20	EPL_HBURD_20	F_HBURD_20	E_NOHSDP_20	ET_EDSTATUS_20	EP_NOHSDP_20	EPL_NOHSDP_20	F_NOHSDP_20	E_UNINSUR_20	ET_INSURSTATUS_20	EP_UNINSUR_20	EPL_UNINSUR_20	F_UNINSUR_20	E_AGE65_20	EP_AGE65_20	EPL_AGE65_20	F_AGE65_20	E_AGE17_20	EP_AGE17_20	EPL_AGE17_20	E_DISABL_20	ET_DISABLSTATUS_20	EP_DISABL_20	EPL_DISABL_20	F_DISABL_20	E_SNGPNT_20	ET_FAMILIES_20	EP_SNGPNT_20	EPL_SNGPNT_20	F_SNGPNT_20	E_LIMENG_20	ET_POPAGE5UP_20	EP_LIMENG_20	EPL_LIMENG_20	E_MINRTY_20	ET_POPETHRACE_20	EP_MINRTY_20	EPL_MINRTY_20	F_MINRTY_20	E_STRHU_20	E_MUNIT_20	EP_MUNIT_20	EPL_MUNIT_20	F_MUNIT_20	E_MOBILE_20	EP_MOBILE_20	EPL_MOBILE_20	F_MOBILE_20	E_CROWD_20	ET_OCCUPANTS_20	EP_CROWD_20	EPL_CROWD_20	E_NOVEH_20	ET_KNOWNVEH_20	EP_NOVEH_20	EPL_NOVEH_20	F_NOVEH_20	E_GROUPQ_20	ET_HHTYPE_20	EP_GROUPQ_20	EPL_GROUPQ_20	F_GROUPQ_20	SPL_THEME1_20	RPL_THEME1_20	F_THEME1_20	SPL_THEME2_20	RPL_THEME2_20	F_THEME2_20	SPL_THEME3_20	RPL_THEME3_20	F_THEME3_20	SPL_THEME4_20	RPL_THEME4_20	F_THEME4_20	SPL_THEMES_20	RPL_THEMES_20	F_TOTAL_20	lihtc_eligibility	NAME	Median_Income_10	Median_Home_Value_10	Median_Income_19	Median_Home_Value_19	Median_Income_10adj	Median_Home_Value_10adj	Median_Income_Change	Median_Income_Change_pct	Median_Home_Value_Change	Median_Home_Value_Change_pct	housing_price_index10	housing_price_index20	county_title	cbsa	cbsa_code
01005950700	01005	950700	AL	Alabama	Barbour County	3	South Region	6	East South Central Division	1753	687	563	615	1628	37.77641	0.8088	1	17	667	2.548726	0.06941	0	41	376	10.90426	0.01945	0	62	187	33.15508	0.24640	0	103	563	18.29485	0.04875	0	264	1208	21.85430	0.7570	1	201	1527	13.163065	0.4991	0	368	20.992584	0.89510	1	462	26.354820	0.66130	211	1085	19.44700	0.7505	1	107	399	26.81704	0.8048	1	0	1628	0.0000000	0.09298	861	1753	49.11580	0.7101	0	687	17	2.4745269	0.4324	0	38	5.5312955	0.6970	0	3	563	0.5328597	0.3037	0	19	563	3.374778	0.3529	0	233	1753	13.29150	0.9517	1	2.18306	0.4137	2	3.20468	0.8377	3	0.7101	0.7035	0	2.7377	0.6100	1	8.83554	0.6264	6	1527	691	595	565	1365	41.39194	0.8765	1	37	572	6.468532	0.6776	0	70	376	18.617021	0.38590	0	92	219	42.009132	0.47360	0	162	595	27.22689	0.44540	0	280	1114	25.13465	0.8942	1	105	1378	7.619739	0.5505	0	383	25.081860	0.88450	1	337	22.069417	0.51380	237	1041.0000	22.76657	0.8360	1	144	413.0000	34.86683	0.9114	1	11	1466	0.7503411	0.40700	711	1527.0000	46.56189	0.6441	0	691	13	1.8813314	0.3740	0	37	5.3545586	0.7152	0	0	595	0.0000000	0.09796	115	595.0000	19.327731	0.8859	1	149	1527	9.7576948	0.9470	1	3.44420	0.7707	2	3.55270	0.9403	3	0.6441	0.6387	0	3.02006	0.7337	2	10.66106	0.8537	7	Yes	Census Tract 9507, Barbour County, Alabama	15257	133700	17244	137500	17698.12	155092	-454.12	-0.0256592	-17592	-0.1134294	131.05	135.61	Barbour County, Alabama	Eufaula, AL-GA MicroSA	C2164
01011952100	01011	952100	AL	Alabama	Bullock County	3	South Region	6	East South Central Division	1652	796	554	564	1652	34.14044	0.7613	1	46	816	5.637255	0.33630	0	96	458	20.96070	0.19930	0	62	96	64.58333	0.89170	1	158	554	28.51986	0.29220	0	271	1076	25.18587	0.8163	1	155	1663	9.320505	0.3183	0	199	12.046005	0.47180	0	420	25.423729	0.60240	327	1279	25.56685	0.9151	1	137	375	36.53333	0.9108	1	0	1590	0.0000000	0.09298	1428	1652	86.44068	0.8939	1	796	0	0.0000000	0.1224	0	384	48.2412060	0.9897	1	19	554	3.4296029	0.7145	0	45	554	8.122744	0.6556	0	0	1652	0.00000	0.3640	0	2.52440	0.5138	2	2.99308	0.7515	2	0.8939	0.8856	1	2.8462	0.6637	1	9.25758	0.6790	6	1382	748	549	742	1382	53.69030	0.9560	1	40	511	7.827789	0.7730	1	110	402	27.363184	0.71780	0	45	147	30.612245	0.23070	0	155	549	28.23315	0.47730	0	181	905	20.00000	0.8253	1	232	1382	16.787265	0.8813	1	164	11.866860	0.27170	0	250	18.089725	0.26290	258	1132.0000	22.79152	0.8368	1	99	279.0000	35.48387	0.9162	1	33	1275	2.5882353	0.64520	1347	1382.0000	97.46744	0.9681	1	748	0	0.0000000	0.1079	0	375	50.1336898	0.9922	1	0	549	0.0000000	0.09796	37	549.0000	6.739526	0.6039	0	0	1382	0.0000000	0.1831	0	3.91290	0.8785	4	2.93280	0.7342	2	0.9681	0.9599	1	1.98506	0.2471	1	9.79886	0.7570	8	Yes	Census Tract 9521, Bullock County, Alabama	19754	58200	18598	66900	22914.64	67512	-4316.64	-0.1883791	-612	-0.0090651	NA	NA	NA	NA	NA
01015000300	01015	000300	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	3074	1635	1330	1904	3067	62.08021	0.9710	1	293	1362	21.512482	0.96630	1	180	513	35.08772	0.65450	0	383	817	46.87882	0.55040	0	563	1330	42.33083	0.70280	0	720	2127	33.85049	0.9148	1	628	2835	22.151675	0.8076	1	380	12.361744	0.49340	0	713	23.194535	0.45030	647	2111	30.64898	0.9708	1	298	773	38.55110	0.9247	1	0	2878	0.0000000	0.09298	2623	3074	85.32856	0.8883	1	1635	148	9.0519878	0.6465	0	6	0.3669725	0.4502	0	68	1330	5.1127820	0.8082	1	303	1330	22.781955	0.9029	1	0	3074	0.00000	0.3640	0	4.36250	0.9430	4	2.93218	0.7233	2	0.8883	0.8800	1	3.1718	0.8070	2	11.35478	0.9009	9	2390	1702	1282	1287	2390	53.84937	0.9566	1	102	1066	9.568480	0.8541	1	158	609	25.944171	0.67520	0	286	673	42.496285	0.48560	0	444	1282	34.63339	0.66340	0	467	1685	27.71513	0.9180	1	369	2379	15.510719	0.8562	1	342	14.309623	0.40850	0	548	22.928870	0.57100	647	1831.0000	35.33588	0.9862	1	202	576.0000	35.06944	0.9130	1	16	2134	0.7497657	0.40690	1896	2390.0000	79.33054	0.8451	1	1702	96	5.6404230	0.5329	0	0	0.0000000	0.2186	0	0	1282	0.0000000	0.09796	186	1282.0000	14.508580	0.8308	1	43	2390	1.7991632	0.7727	1	4.24830	0.9395	4	3.28560	0.8773	2	0.8451	0.8379	1	2.45296	0.4602	2	10.83196	0.8718	9	Yes	Census Tract 3, Calhoun County, Alabama	12211	41700	18299	51300	14164.76	48372	4134.24	0.2918680	2928	0.0605309	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015000500	01015	000500	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	1731	1175	743	1042	1619	64.36072	0.9767	1	124	472	26.271186	0.98460	1	136	461	29.50108	0.48970	0	163	282	57.80142	0.79190	1	299	743	40.24226	0.64910	0	340	1270	26.77165	0.8389	1	460	1794	25.641026	0.8722	1	271	15.655690	0.70190	0	368	21.259388	0.32190	507	1449	34.98965	0.9885	1	150	386	38.86010	0.9269	1	0	1677	0.0000000	0.09298	1559	1731	90.06355	0.9123	1	1175	50	4.2553191	0.5128	0	4	0.3404255	0.4480	0	0	743	0.0000000	0.1238	0	122	743	16.419919	0.8473	1	0	1731	0.00000	0.3640	0	4.32150	0.9362	4	3.03218	0.7679	2	0.9123	0.9038	1	2.2959	0.3818	1	10.56188	0.8244	8	940	907	488	586	940	62.34043	0.9815	1	59	297	19.865320	0.9833	1	100	330	30.303030	0.79220	1	58	158	36.708861	0.34970	0	158	488	32.37705	0.60200	0	199	795	25.03145	0.8930	1	118	940	12.553192	0.7770	1	246	26.170213	0.90530	1	118	12.553192	0.08233	383	822.5089	46.56484	0.9984	1	30	197.8892	15.16000	0.5363	0	0	889	0.0000000	0.09479	898	940.3866	95.49264	0.9489	1	907	0	0.0000000	0.1079	0	2	0.2205072	0.4456	0	2	488	0.4098361	0.23670	146	487.6463	29.939736	0.9404	1	0	940	0.0000000	0.1831	0	4.23680	0.9379	4	2.61712	0.5593	2	0.9489	0.9409	1	1.91370	0.2196	1	9.71652	0.7468	8	Yes	Census Tract 5, Calhoun County, Alabama	11742	38800	13571	38800	13620.72	45008	-49.72	-0.0036503	-6208	-0.1379310	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015000600	01015	000600	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	2571	992	796	1394	2133	65.35396	0.9789	1	263	905	29.060773	0.98990	1	121	306	39.54248	0.75940	1	209	490	42.65306	0.44810	0	330	796	41.45729	0.68030	0	641	1556	41.19537	0.9554	1	416	1760	23.636364	0.8383	1	220	8.556982	0.24910	0	584	22.714897	0.41610	539	1353	39.83740	0.9955	1	243	466	52.14592	0.9783	1	30	2366	1.2679628	0.48990	1944	2571	75.61260	0.8440	1	992	164	16.5322581	0.7673	1	8	0.8064516	0.5110	0	46	796	5.7788945	0.8329	1	184	796	23.115578	0.9049	1	614	2571	23.88176	0.9734	1	4.44280	0.9548	4	3.12890	0.8088	2	0.8440	0.8362	1	3.9895	0.9792	4	12.40520	0.9696	11	1950	964	719	837	1621	51.63479	0.9467	1	157	652	24.079755	0.9922	1	22	364	6.043956	0.01547	0	129	355	36.338028	0.34200	0	151	719	21.00139	0.23030	0	363	1387	26.17159	0.9048	1	351	1613	21.760694	0.9435	1	249	12.769231	0.32090	0	356	18.256410	0.27140	332	1259.7041	26.35540	0.9135	1	136	435.6156	31.22018	0.8775	1	0	1891	0.0000000	0.09479	1463	1949.9821	75.02633	0.8219	1	964	14	1.4522822	0.3459	0	8	0.8298755	0.5269	0	19	719	2.6425591	0.61120	197	719.0542	27.397100	0.9316	1	329	1950	16.8717949	0.9655	1	4.01750	0.9001	4	2.47809	0.4764	2	0.8219	0.8149	1	3.38110	0.8712	2	10.69859	0.8583	9	Yes	Census Tract 6, Calhoun County, Alabama	10958	48000	14036	43300	12711.28	55680	1324.72	0.1042161	-12380	-0.2223420	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015002101	01015	002101	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	3872	1454	1207	1729	2356	73.38710	0.9916	1	489	2020	24.207921	0.97860	1	20	168	11.90476	0.02541	0	718	1039	69.10491	0.93320	1	738	1207	61.14333	0.96900	1	113	725	15.58621	0.6035	0	664	3943	16.839970	0.6495	0	167	4.313016	0.05978	0	238	6.146694	0.02255	264	2359	11.19118	0.3027	0	94	263	35.74144	0.9050	1	46	3769	1.2204829	0.48250	1601	3872	41.34814	0.6572	0	1454	761	52.3383769	0.9504	1	65	4.4704264	0.6738	0	5	1207	0.4142502	0.2791	0	113	1207	9.362055	0.7004	0	1516	3872	39.15289	0.9860	1	4.19220	0.9133	3	1.77253	0.1304	1	0.6572	0.6511	0	3.5897	0.9337	2	10.21163	0.7885	6	3238	1459	1014	1082	1836	58.93246	0.9735	1	251	1403	17.890235	0.9767	1	31	155	20.000000	0.44920	0	515	859	59.953434	0.85540	1	546	1014	53.84615	0.95350	1	134	916	14.62882	0.7033	0	251	3238	7.751699	0.5588	0	167	5.157505	0.03597	0	169	5.219271	0.02111	323	1667.0000	19.37612	0.7205	0	94	277.0000	33.93502	0.9040	1	0	3164	0.0000000	0.09479	1045	3238.0000	32.27301	0.5125	0	1459	607	41.6038382	0.9185	1	65	4.4551062	0.6949	0	24	1014	2.3668639	0.57900	85	1014.0000	8.382643	0.6775	0	1402	3238	43.2983323	0.9876	1	4.16580	0.9263	3	1.77637	0.1225	1	0.5125	0.5082	0	3.85750	0.9661	2	10.31217	0.8160	6	Yes	Census Tract 21.01, Calhoun County, Alabama	4968	92000	9312	153500	5762.88	106720	3549.12	0.6158587	46780	0.4383433	NA	NA	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01015002300	01015	002300	AL	Alabama	Calhoun County	3	South Region	6	East South Central Division	3882	1861	1608	1366	3882	35.18805	0.7753	1	186	1539	12.085770	0.80740	1	284	1109	25.60866	0.35530	0	202	499	40.48096	0.39670	0	486	1608	30.22388	0.34700	0	727	2610	27.85441	0.8534	1	547	3706	14.759849	0.5669	0	716	18.444101	0.82530	1	904	23.286966	0.45720	719	2919	24.63172	0.8986	1	207	1191	17.38035	0.5923	0	0	3720	0.0000000	0.09298	490	3882	12.62236	0.3118	0	1861	38	2.0419130	0.4070	0	199	10.6931757	0.7836	1	52	1608	3.2338308	0.6986	0	166	1608	10.323383	0.7304	0	0	3882	0.00000	0.3640	0	3.35000	0.7384	3	2.86638	0.6919	2	0.3118	0.3089	0	2.9836	0.7289	1	9.51178	0.7100	6	3265	1774	1329	1103	3265	33.78254	0.7880	1	122	1422	8.579465	0.8131	1	101	844	11.966825	0.10960	0	126	485	25.979381	0.15930	0	227	1329	17.08051	0.11070	0	267	2122	12.58247	0.6388	0	328	3265	10.045942	0.6808	0	440	13.476263	0.36070	0	843	25.819296	0.74470	530	2422.0000	21.88274	0.8097	1	254	861.0000	29.50058	0.8574	1	0	3026	0.0000000	0.09479	811	3265.0000	24.83920	0.4221	0	1774	7	0.3945885	0.2444	0	338	19.0529876	0.8924	1	19	1329	1.4296464	0.44520	120	1329.0000	9.029345	0.7016	0	0	3265	0.0000000	0.1831	0	3.03140	0.6608	2	2.86729	0.7016	2	0.4221	0.4185	0	2.46670	0.4669	1	8.78749	0.6230	5	Yes	Census Tract 23, Calhoun County, Alabama	15086	77500	21540	78500	17499.76	89900	4040.24	0.2308740	-11400	-0.1268076	120.54	131.82	Calhoun County, Alabama	Anniston-Oxford, AL MSA	C1150
01023956700	01023	956700	AL	Alabama	Choctaw County	3	South Region	6	East South Central Division	3011	1772	1179	1715	3011	56.95782	0.9531	1	266	890	29.887640	0.99100	1	267	1035	25.79710	0.36240	0	79	144	54.86111	0.73440	0	346	1179	29.34690	0.31850	0	738	2053	35.94739	0.9287	1	543	2904	18.698347	0.7133	0	569	18.897376	0.84040	1	648	21.521089	0.33840	813	2273	35.76771	0.9901	1	252	771	32.68482	0.8778	1	0	2880	0.0000000	0.09298	2455	3011	81.53437	0.8712	1	1772	38	2.1444695	0.4136	0	485	27.3702032	0.9349	1	72	1179	6.1068702	0.8435	1	109	1179	9.245123	0.6964	0	0	3011	0.00000	0.3640	0	3.90460	0.8597	3	3.13968	0.8131	3	0.8712	0.8631	1	3.2524	0.8387	2	11.16788	0.8840	9	3335	1912	1362	1135	3313	34.25898	0.7948	1	188	1147	16.390584	0.9686	1	212	1058	20.037807	0.45090	0	27	304	8.881579	0.02679	0	239	1362	17.54772	0.12350	0	466	2537	18.36815	0.7948	1	495	3335	14.842579	0.8413	1	791	23.718141	0.85250	1	613	18.380810	0.27840	884	2714.0000	32.57185	0.9752	1	230	918.0000	25.05447	0.7925	1	25	3103	0.8056719	0.41920	2637	3335.0000	79.07046	0.8436	1	1912	0	0.0000000	0.1079	0	758	39.6443515	0.9799	1	16	1362	1.1747430	0.40060	75	1362.0000	5.506608	0.5316	0	8	3335	0.2398801	0.4965	0	3.52300	0.7901	4	3.31780	0.8870	3	0.8436	0.8365	1	2.51650	0.4924	1	10.20090	0.8033	9	Yes	Census Tract 9567, Choctaw County, Alabama	12737	60900	16852	63400	14774.92	70644	2077.08	0.1405815	-7244	-0.1025423	NA	NA	NA	NA	NA
01023957000	01023	957000	AL	Alabama	Choctaw County	3	South Region	6	East South Central Division	2567	1187	916	767	2567	29.87924	0.6933	0	145	1060	13.679245	0.86050	1	101	719	14.04729	0.04540	0	43	197	21.82741	0.09791	0	144	916	15.72052	0.02333	0	355	1704	20.83333	0.7366	0	289	2296	12.587108	0.4736	0	324	12.621737	0.51120	0	688	26.801714	0.68810	572	1746	32.76060	0.9809	1	121	636	19.02516	0.6414	0	5	2283	0.2190101	0.22520	1314	2567	51.18816	0.7225	0	1187	0	0.0000000	0.1224	0	335	28.2224094	0.9394	1	13	916	1.4192140	0.4834	0	70	916	7.641921	0.6353	0	0	2567	0.00000	0.3640	0	2.78733	0.5903	1	3.04680	0.7745	1	0.7225	0.7158	0	2.5445	0.5114	1	9.10113	0.6601	3	2077	1158	866	759	2072	36.63127	0.8256	1	61	780	7.820513	0.7726	1	106	735	14.421769	0.19760	0	11	131	8.396947	0.02525	0	117	866	13.51039	0.04053	0	351	1464	23.97541	0.8815	1	205	2077	9.870005	0.6729	0	402	19.354839	0.68820	0	496	23.880597	0.63430	466	1576.0000	29.56853	0.9544	1	154	612.0000	25.16340	0.7942	1	0	2002	0.0000000	0.09479	1018	2077.0000	49.01300	0.6638	0	1158	0	0.0000000	0.1079	0	439	37.9101900	0.9766	1	0	866	0.0000000	0.09796	42	866.0000	4.849884	0.4884	0	5	2077	0.2407318	0.4971	0	3.19313	0.7061	3	3.16589	0.8369	2	0.6638	0.6582	0	2.16796	0.3247	1	9.19078	0.6792	6	Yes	Census Tract 9570, Choctaw County, Alabama	16224	51600	21740	74000	18819.84	59856	2920.16	0.1551639	14144	0.2363005	NA	NA	NA	NA	NA
01031010500	01031	010500	AL	Alabama	Coffee County	3	South Region	6	East South Central Division	4529	1950	1664	1649	4022	40.99950	0.8432	1	114	1424	8.005618	0.56260	0	309	1057	29.23368	0.48130	0	251	607	41.35091	0.41690	0	560	1664	33.65385	0.45740	0	1269	3370	37.65579	0.9387	1	516	4279	12.058892	0.4492	0	832	18.370501	0.82310	1	894	19.739457	0.23950	1023	3404	30.05288	0.9666	1	303	1112	27.24820	0.8108	1	43	4270	1.0070258	0.44510	1761	4529	38.88276	0.6383	0	1950	6	0.3076923	0.2576	0	276	14.1538462	0.8279	1	8	1664	0.4807692	0.2925	0	125	1664	7.512019	0.6289	0	507	4529	11.19452	0.9441	1	3.25110	0.7138	2	3.28510	0.8639	3	0.6383	0.6324	0	2.9510	0.7136	2	10.12550	0.7794	7	4815	2118	1731	1329	4470	29.73154	0.7256	0	147	1903	7.724645	0.7670	1	209	1256	16.640127	0.29310	0	208	475	43.789474	0.51620	0	417	1731	24.09012	0.33700	0	953	3728	25.56330	0.8985	1	668	4485	14.894091	0.8425	1	1053	21.869159	0.79500	1	766	15.908619	0.16760	1010	3719.0000	27.15784	0.9262	1	243	1133.0000	21.44748	0.7184	0	1	4577	0.0218484	0.19150	1643	4815.0000	34.12253	0.5321	0	2118	0	0.0000000	0.1079	0	475	22.4268178	0.9157	1	37	1731	2.1374928	0.55080	144	1731.0000	8.318891	0.6750	0	330	4815	6.8535826	0.9282	1	3.57060	0.8018	3	2.79870	0.6649	2	0.5321	0.5276	0	3.17760	0.7990	2	10.07900	0.7892	7	Yes	Census Tract 105, Coffee County, Alabama	14641	88000	21367	78100	16983.56	102080	4383.44	0.2580990	-23980	-0.2349138	128.88	137.26	Coffee County, Alabama	Dothan-Enterprise-Ozark, AL CSA	CS222

Log NMTC and LIHTC Variables

svi_national_nmtc_df$Median_Income_10adj_log <- log(svi_national_nmtc_df$Median_Income_10adj)
svi_national_nmtc_df$Median_Income_19_log <- log(svi_national_nmtc_df$Median_Income_19)

svi_national_nmtc_df$Median_Home_Value_10adj_log = log(svi_national_nmtc_df$Median_Home_Value_10adj)
svi_national_nmtc_df$Median_Home_Value_19_log = log(svi_national_nmtc_df$Median_Home_Value_19)

svi_national_nmtc_df$housing_price_index10_log = log(svi_national_nmtc_df$housing_price_index10)
svi_national_nmtc_df$housing_price_index20_log = log(svi_national_nmtc_df$housing_price_index20)

svi_divisional_nmtc_df$Median_Income_10adj_log <- log(svi_divisional_nmtc_df$Median_Income_10adj)
svi_divisional_nmtc_df$Median_Income_19_log <- log(svi_divisional_nmtc_df$Median_Income_19)

svi_divisional_nmtc_df$Median_Home_Value_10adj_log = log(svi_divisional_nmtc_df$Median_Home_Value_10adj)
svi_divisional_nmtc_df$Median_Home_Value_19_log = log(svi_divisional_nmtc_df$Median_Home_Value_19)

svi_divisional_nmtc_df$housing_price_index10_log = log(svi_divisional_nmtc_df$housing_price_index10)
svi_divisional_nmtc_df$housing_price_index20_log = log(svi_divisional_nmtc_df$housing_price_index20)

svi_national_lihtc_df$Median_Income_10adj_log <- log(svi_national_lihtc_df$Median_Income_10adj)
svi_national_lihtc_df$Median_Income_19_log <- log(svi_national_lihtc_df$Median_Income_19)

svi_national_lihtc_df$Median_Home_Value_10adj_log = log(svi_national_lihtc_df$Median_Home_Value_10adj)
svi_national_lihtc_df$Median_Home_Value_19_log = log(svi_national_lihtc_df$Median_Home_Value_19)

svi_national_lihtc_df$housing_price_index10_log = log(svi_national_lihtc_df$housing_price_index10)
svi_national_lihtc_df$housing_price_index20_log = log(svi_national_lihtc_df$housing_price_index20)

svi_divisional_lihtc_df$Median_Income_10adj_log <- log(svi_divisional_lihtc_df$Median_Income_10adj)
svi_divisional_lihtc_df$Median_Income_19_log <- log(svi_divisional_lihtc_df$Median_Income_19)

svi_divisional_lihtc_df$Median_Home_Value_10adj_log = log(svi_divisional_lihtc_df$Median_Home_Value_10adj)
svi_divisional_lihtc_df$Median_Home_Value_19_log = log(svi_divisional_lihtc_df$Median_Home_Value_19)

svi_divisional_lihtc_df$housing_price_index10_log = log(svi_divisional_lihtc_df$housing_price_index10)
svi_divisional_lihtc_df$housing_price_index20_log = log(svi_divisional_lihtc_df$housing_price_index20)

Diff-in-Diff Models

NMTC Evaluation

Divisional SVI

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010
nmtc_did10_div_svi <- svi_divisional_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_10, F_THEME2_10, F_THEME3_10, F_THEME4_10, F_TOTAL_10, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_10",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_10",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_10",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_10",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_10") 

nrow(nmtc_did10_div_svi)

## [1] 4237

# Create 2020 df, create post variable and set to 1, create year variable and set to 2020
nmtc_did20_div_svi <- svi_divisional_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_20, F_THEME2_20, F_THEME3_20, F_THEME4_20, F_TOTAL_20, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_20",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_20",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_20",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_20",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_20"
  )


nrow(nmtc_did20_div_svi)

## [1] 4237

nmtc_diff_in_diff_div_svi <- bind_rows(nmtc_did10_div_svi, nmtc_did20_div_svi)

nmtc_diff_in_diff_div_svi <- nmtc_diff_in_diff_div_svi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_svi)

## [1] 8474

Divisional Median Income

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010, remove any tracts that don't have data for 2010 and 2019
nmtc_did10_div_inc <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_10adj_log") 


nrow(nmtc_did10_div_inc)

## [1] 4234

# Create 2019 df, create post variable and set to 1, create year variable and set to 2019, remove any tracts that don't have data for 2010 and 2019
nmtc_did19_div_inc <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_19_log") 


nrow(nmtc_did19_div_inc)

## [1] 4234

nmtc_diff_in_diff_div_inc <- bind_rows(nmtc_did10_div_inc, nmtc_did19_div_inc)

nmtc_diff_in_diff_div_inc <- nmtc_diff_in_diff_div_inc %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_inc)

## [1] 8468

Divisional Home Value

nmtc_did10_div_mhv <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_10adj_log") 


nrow(nmtc_did10_div_mhv)

## [1] 4074

nmtc_did19_div_mhv <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_19_log") 


nrow(nmtc_did19_div_mhv)

## [1] 4074

nmtc_diff_in_diff_div_mhv <- bind_rows(nmtc_did10_div_mhv, nmtc_did19_div_mhv)

nmtc_diff_in_diff_div_mhv <- nmtc_diff_in_diff_div_mhv %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_mhv)

## [1] 8148

Divisional House Price Index

nmtc_did10_div_hpi <- svi_divisional_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index10_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index10_log") 


nrow(nmtc_did10_div_hpi)

## [1] 2829

nmtc_did20_div_hpi <- svi_divisional_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index20_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index20_log") 


nrow(nmtc_did20_div_hpi)

## [1] 2829

nmtc_diff_in_diff_div_hpi <- bind_rows(nmtc_did10_div_hpi, nmtc_did20_div_hpi)

nmtc_diff_in_diff_div_hpi <- nmtc_diff_in_diff_div_hpi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_hpi)

## [1] 5658

NMTC Divisional Model

# SVI & Economic Models

m1_nmtc_div <- lm( SVI_FLAG_COUNT_SES ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m2_nmtc_div <- lm( SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m3_nmtc_div <- lm( SVI_FLAG_COUNT_REM ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m4_nmtc_div <- lm( SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m5_nmtc_div <- lm( SVI_FLAG_COUNT_OVERALL  ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi)

m6_nmtc_div <- lm( MEDIAN_INCOME ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_inc )

m7_nmtc_div <- lm( MEDIAN_HOME_VALUE ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_mhv )

m8_nmtc_div <- lm( HOUSE_PRICE_INDEX ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_hpi )

# Add all models to a list
models <- list(
  
  "SES" = m1_nmtc_div,
  "HHChar"  = m2_nmtc_div,
  "REM" = m3_nmtc_div,
  "HOUSETRANSPT" = m4_nmtc_div,
  "OVERALL" = m5_nmtc_div,
  "Median Income (USD, logged)" = m6_nmtc_div,
  "Median Home Value (USD, logged)" = m7_nmtc_div,
  "House Price Index (logged)" = m8_nmtc_div
)


# Display model results
modelsummary(models,  fmt = 2, stars = c('*' = .05, '**' = .01, '***' = .001), coef_omit = "cbsa", gof_omit = "IC|Log",
             notes = list('All models include metro-level fixed effects by core-based statistical area (cbsa).'),
             title = paste0("Differences-in-Differences Linear Regression Analysis of NMTC in ", census_division)) %>%
  group_tt(j = list("Social Vulnerability" = 2:6, "Economic Outcomes" = 7:9))

Differences-in-Differences Linear Regression Analysis of NMTC in Pacific Division
	Social Vulnerability					Economic Outcomes
	SES	HHChar	REM	HOUSETRANSPT	OVERALL	Median Income (USD, logged)	Median Home Value (USD, logged)	House Price Index (logged)
(Intercept)	1.10***	1.88***	0.05	1.15***	4.18***	10.13***	11.89***	5.12***
	(0.31)	(0.23)	(0.09)	(0.23)	(0.62)	(0.06)	(0.09)	(0.10)
treat	0.63***	0.24***	0.15***	0.40***	1.43***	-0.14***	-0.00	-0.04
	(0.10)	(0.07)	(0.03)	(0.07)	(0.20)	(0.02)	(0.03)	(0.04)
post	-0.06	-0.09***	0.00	-0.03	-0.17**	0.04***	-0.05***	0.65***
	(0.03)	(0.02)	(0.01)	(0.02)	(0.06)	(0.01)	(0.01)	(0.01)
treat × post	-0.05	-0.05	-0.01	-0.01	-0.12	0.06*	0.00	-0.02
	(0.14)	(0.10)	(0.04)	(0.10)	(0.28)	(0.03)	(0.04)	(0.05)
Num.Obs.	8240	8240	8240	8240	8240	8236	7916	5526
R2	0.270	0.112	0.301	0.060	0.233	0.182	0.417	0.488
R2 Adj.	0.264	0.104	0.295	0.052	0.227	0.175	0.412	0.481
RMSE	1.36	1.01	0.41	1.04	2.77	0.27	0.40	0.40
p < 0.05, p < 0.01, * p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).

Differences-in-Differences Linear Regression Analysis of NMTC in Pacific Division

In the social vulnerability model, none of the categories returned statistically significant changes. In the economic conditions model, only median income was statistically significant. Median income was greater in tracts that received NMTC dollars compared to eligible tracts that didn’t. There was an increase of 6% (0.06*100) for each 1-unit increase.

The social vulnerability model results mean we can’t conclude there was a program impact in the Pacific Division. However, it’s important to note that the treatment group (counties that participated in NMTC) were more likely to have received more SVI flags across all four themes and overall.

Visualize Median Income

status <- c("NMTC Non-Participant", 
             "NMTC Participant Counterfactual", 
             "NMTC Participant", 
             "NMTC Non-Participant", 
             "NMTC Participant Counterfactual", 
             "NMTC Participant")
year <- c(2010, 
          2010, 
          2010, 
          2020, 
          2020, 
          2020)
outcome <- c(exp(m6_nmtc_div$coefficients[1]), 
           exp(m6_nmtc_div$coefficients[1])*exp(m6_nmtc_div$coefficients[2]), 
           exp(m6_nmtc_div$coefficients[1])*exp(m6_nmtc_div$coefficients[2]),
           exp(m6_nmtc_div$coefficients[1])*exp(m6_nmtc_div$coefficients[3]), 
           exp(m6_nmtc_div$coefficients[1])*exp(m6_nmtc_div$coefficients[2])*exp(m6_nmtc_div$coefficients[3]),
           exp(m6_nmtc_div$coefficients[1])*exp(m6_nmtc_div$coefficients[2])*exp(m6_nmtc_div$coefficients[3])*exp(m6_nmtc_div$coefficients[length(m6_nmtc_div$coefficients)])
)

svidiv_viz_medinc_nmtc <- data.frame(status, year, outcome)

### Note that instead of rounding like we did for SVI variables, we will be formatting our outcome as US dollars
svidiv_viz_medinc_nmtc$outcome_label <- scales::dollar_format()(svidiv_viz_medinc_nmtc$outcome)

svidiv_viz_medinc_nmtc

##                            status year  outcome outcome_label
## 1            NMTC Non-Participant 2010 24986.85    $24,986.85
## 2 NMTC Participant Counterfactual 2010 21799.86    $21,799.86
## 3                NMTC Participant 2010 21799.86    $21,799.86
## 4            NMTC Non-Participant 2020 26029.35    $26,029.35
## 5 NMTC Participant Counterfactual 2020 22709.39    $22,709.39
## 6                NMTC Participant 2020 24050.91    $24,050.91

slopegraph_plot(svidiv_viz_medinc_nmtc, "NMTC Participant", "NMTC Non-Participant", "Impact of NMTC Program on Average Median Income", paste0(census_division, " | 2010 - 2020"))

From the slopegraph, we can see that incomes in increased across the board. However, the gap between the participant counterfactual and the actual outcome is statistically significant. NMTC participants would be expected see median household income increase from $21,799.86 in 2010 to $22,709.39 in 2020. However, participation in NMTC increase the rate of growth, which saw the average median household income for participating counties rise to $24,050.91.

LIHTC Evaluation

Divisional SVI

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010
lihtc_did10_div_svi <- svi_divisional_lihtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_10, F_THEME2_10, F_THEME3_10, F_THEME4_10, F_TOTAL_10, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_10",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_10",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_10",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_10",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_10") 

nrow(lihtc_did10_div_svi)

## [1] 584

# Create 2020 df, create post variable and set to 1, create year variable and set to 2020
lihtc_did20_div_svi <- svi_divisional_lihtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_20, F_THEME2_20, F_THEME3_20, F_THEME4_20, F_TOTAL_20, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "lihtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_20",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_20",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_20",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_20",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_20"
  )


nrow(lihtc_did20_div_svi)

## [1] 584

lihtc_diff_in_diff_div_svi <- bind_rows(lihtc_did10_div_svi, lihtc_did20_div_svi)

lihtc_diff_in_diff_div_svi <- lihtc_diff_in_diff_div_svi %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_svi)

## [1] 1168

Divisional Median Income

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010, remove any tracts that don't have data for 2010 and 2019
lihtc_did10_div_inc <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_10adj_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_INCOME" = "Median_Income_10adj_log") 


nrow(lihtc_did10_div_inc)

## [1] 583

# Create 2019 df, create post variable and set to 1, create year variable and set to 2019, remove any tracts that don't have data for 2010 and 2019
lihtc_did19_div_inc <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_19_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_INCOME" = "Median_Income_19_log") 


nrow(lihtc_did19_div_inc)

## [1] 583

lihtc_diff_in_diff_div_inc <- bind_rows(lihtc_did10_div_inc, lihtc_did19_div_inc)

lihtc_diff_in_diff_div_inc <- lihtc_diff_in_diff_div_inc %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_inc)

## [1] 1166

Divisional Median Home Value

lihtc_did10_div_mhv <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_10adj_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_10adj_log") 


nrow(lihtc_did10_div_mhv)

## [1] 543

lihtc_did19_div_mhv <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_19_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_19_log") 


nrow(lihtc_did19_div_mhv)

## [1] 543

lihtc_diff_in_diff_div_mhv <- bind_rows(lihtc_did10_div_mhv, lihtc_did19_div_mhv)

lihtc_diff_in_diff_div_mhv <- lihtc_diff_in_diff_div_mhv %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_mhv)

## [1] 1086

Divisional House Price Index

lihtc_did10_div_hpi <- svi_divisional_lihtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index10_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index10_log") 


nrow(lihtc_did10_div_hpi)

## [1] 310

lihtc_did20_div_hpi <- svi_divisional_lihtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index20_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "lihtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index20_log") 


nrow(lihtc_did20_div_hpi)

## [1] 310

lihtc_diff_in_diff_div_hpi <- bind_rows(lihtc_did10_div_hpi, lihtc_did20_div_hpi)

lihtc_diff_in_diff_div_hpi <- lihtc_diff_in_diff_div_hpi %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_hpi)

## [1] 620

LIHTC Divisional Model

# SVI & Economic Models

m1_lihtc_div <- lm( SVI_FLAG_COUNT_SES ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m2_lihtc_div <- lm( SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m3_lihtc_div <- lm( SVI_FLAG_COUNT_REM ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m4_lihtc_div <- lm( SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m5_lihtc_div <- lm( SVI_FLAG_COUNT_OVERALL  ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi)

m6_lihtc_div <- lm( MEDIAN_INCOME ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_inc )

m7_lihtc_div <- lm( MEDIAN_HOME_VALUE ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_mhv )

m8_lihtc_div <- lm( HOUSE_PRICE_INDEX ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_hpi )

# Add all models to a list
models <- list(
  
  "SES" = m1_lihtc_div,
  "HHChar"  = m2_lihtc_div,
  "REM" = m3_lihtc_div,
  "HOUSETRANSPT" = m4_lihtc_div,
  "OVERALL" = m5_lihtc_div,
  "Median Income (USD, logged)" = m6_lihtc_div,
  "Median Home Value (USD, logged)" = m7_lihtc_div,
  "House Price Index (logged)" = m8_lihtc_div
)


# Display model results
modelsummary(models,  fmt = 2, stars = c('*' = .05, '**' = .01, '***' = .001), coef_omit = "cbsa", gof_omit = "IC|Log",
             notes = list('All models include metro-level fixed effects by core-based statistical area (cbsa).'),
             title = paste0("Differences-in-Differences Linear Regression Analysis of LIHTC in ", census_division)) %>%
  group_tt(j = list("Social Vulnerability" = 2:6, "Economic Outcomes" = 7:9))

Differences-in-Differences Linear Regression Analysis of LIHTC in Pacific Division
	Social Vulnerability					Economic Outcomes
	SES	HHChar	REM	HOUSETRANSPT	OVERALL	Median Income (USD, logged)	Median Home Value (USD, logged)	House Price Index (logged)
(Intercept)	1.59	2.50***	-0.02	2.99***	7.05***	9.86***	11.68***	4.83***
	(0.81)	(0.69)	(0.27)	(0.68)	(1.64)	(0.21)	(0.35)	(0.27)
treat	0.01	0.13	0.03	0.04	0.21	-0.02	0.02	0.00
	(0.12)	(0.10)	(0.04)	(0.10)	(0.24)	(0.03)	(0.05)	(0.05)
post	-0.20**	-0.19**	-0.03	-0.17**	-0.59***	0.07***	-0.06	0.74***
	(0.08)	(0.06)	(0.03)	(0.06)	(0.15)	(0.02)	(0.03)	(0.03)
treat × post	0.01	-0.05	0.01	0.10	0.07	0.02	0.00	-0.05
	(0.17)	(0.14)	(0.06)	(0.14)	(0.34)	(0.04)	(0.07)	(0.08)
Num.Obs.	1146	1146	1146	1146	1146	1146	1066	618
R2	0.241	0.213	0.334	0.062	0.266	0.228	0.350	0.547
R2 Adj.	0.217	0.188	0.313	0.033	0.243	0.203	0.328	0.523
RMSE	1.12	0.95	0.38	0.94	2.26	0.30	0.48	0.37
p < 0.05, p < 0.01, * p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).

Differences-in-Differences Linear Regression Analysis of LIHTC in Pacific Division

Neither of the diff-in-diff models return statistically significant changes for LIHTC participation. We can’t conclude that the program had a measurable impact, but this study alone is not evidence enough to disprove potential effects. Additional research is needed.